Reproducing kernel Hilbert. Nuno Vasconcelos ECE Department, UCSD

Size: px
Start display at page:

Download "Reproducing kernel Hilbert. Nuno Vasconcelos ECE Department, UCSD"

Transcription

1 Reprducng erne Hbert spaces and reguarzatn Nun Vascnces ECE Department, UCSD

2 Casscatn a casscatn prbem has tw types varabes X - vectr bservatns eatures n the wrd Y - state cass the wrd Perceptrn: casser mpements the near decsn rue h sgn[ g ] wth T g w + b w apprprate when the casses are neary separabe t dea wth nn-near separabty we ntrduce a erne b w g w

3 Types ernes these three are equvaent dt prduct erne pstve dente erne Mercer erne 3

4 Dt-prduct ernes Dentn: a mappng : X X R,y,y s a dt-prduct erne and ny,y <Φ,Φy> where Φ: X H, H s a vectr space, <.,.> s a dt-prduct n H X 1 Φ n 1 3 H 4

5 pstve dente and Mercer ernes Dentn:,y s a pstve dente erne n X X and { 1,..., }, X, the Gram matr { 1 } s pstve dente. [ K ], Dentn: a symmetrc mappng : X X R such that, y y ddy 0, s a Mercer erne st s.t. d < 5

6 Tw derent pctures derent dentns ead t derent nterpretatns what the erne des Reprducng erne map Mercer erne map..., 1 m H K, m m ' ' 1 1, g β H M, g d 1 d T g < Φ :., Φ : φ, φ, L T : 1 1 φ where,φ are the egenvaues and egenunctns,y > 0 6

7 The dt-prduct pcture when we use the Gaussan erne K, e the pnt X s mapped nt the Gaussan G,,σI σ H s the space a unctns that are near cmbnatns Gaussans the erne s a dt prduct n H, and a nn-near smarty n X reprducng prperty n H: anagy t near systems X Φ 1 n 1 3 H K 7

8 The Mercer pcture ths s a mappng rm R n t R d X 1 Φ d 1 3 Φ d much mre e t a mut-ayer Perceptrn than bere the ernezed Perceptrn as a neura net Φ 1. Φ. Φ d. 8

9 The reprducng prperty wth ths dentn H K and <.,.> H K, <.,,.> ths s caed the reprducng prperty an anagy s t thn near tme-nvarant systems the dt prduct as a cnvutn., as the Drac deta. as a system nput the equatn abve s the bass a near tme nvarant systems thery eads t reprducng Kerne Hbert Spaces 9

10 reprducng erne Hbert spaces Dentn: a Hbert space s a cmpete dt-prduct space vectr space + dt prduct + mt pnts a Cauchy sequences Dentn: Let H be a Hbert space unctns : X R. H s a RKHS endwed wth dt-prduct <.,.> : X X R such that 1. spans H,.e., { }, { }, such that span.,..., H { }.<.,.,>, H, 10

11 Mercer ernes hw derent are the spaces H K and H M? Therem: Let : X X R be a Mercer erne. Then, there ests an rthnrma set unctns φ φ d δ and a set 0, such that 1, y, y ddy 1 φ φ y 11

12 RK vs Mercer maps nte that r H M we are wrtng r Φ φ e + L + φ φ1 but, snce the φ. are rthnrma, there s a 1-1 map Γ d : r e span { φ.} φ. r e e d and we can wrte Γ Φ φ φ. + L + φ φ , d d d rm hence., maps nt M span{φ.} 1

13 The Mercer pcture we have d Φ Φ X e 1 e d ΤοΦ 1 e 3 e M Γ ΤοΦ., φ 1 φ d Γ 13 φ 3 φ φ d

14 RK vs Mercer maps dene the dt prduct n M s that 1 1 φ, φ φ φ d δ then {φ.} } s a bass, M s a vectr space, any unctn n M can be wrtten as φ and.,.,.,. φ φ φ φ φ, φ φ δ.e., s a reprducng erne n M 14

15 RK vs Mercer maps urthermre, snce., M, any unctns the rm are n M and., g.. β.,, g.,, β β β β m., φ. φ, m m m φ φm φ., φm. φ φ m φ. φ m β, nte that,g H K and ths s the dt prduct we had n H K 15

16 The Mercer pcture urthermre, nte that r n H M φ and snce φ d δ φ φ φ φ 1 1, the dt prduct n H M s g φ φ β g β φ φ β,, 16

17 The Mercer pcture we have d Φ Φ X e 1 e d ΤοΦ 1 e 3 e M H K Γ ΤοΦ.,,, g β φ 1 φ d Γ 17 φ 3 φ φ d g β,

18 RK vs Mercer maps H K M and <.,.> n H K s the same as <.,.> n M Questn: s M H K? need t shw that any H K rm, and, r any sequence { 1,..., d }, φ., φ1 φ1. + L+ φ φ. 1 d d d., 1 1φ 1 1 d φd 1 φ1. M M L M.,. d 1φ 1 d d φd d φd there s an nvertbe P, then φ., H K and M H K P 18

19 RK vs Mercer maps snce > 0 φ1 1 φ d P M L M φ 1 d φ d d 0 d Π s nvertbe when Π s. I Π s nt nvertbe, then 0 0 s.t. φ there s n sequence r whch Π s nvertbe then 0 0 s.t. φ the φ cannt be rthnrma. Hence there must be nvertbe P and M H K. 19

20 The Mercer pcture we have H M d Φ Φ Μ X H M Μ e 1 e d ΤοΦ 1 e 3 e MH K Γ ΤοΦ.,,, g β φ 1 φ d Γ 0 φ 3 φ φ d g β,

21 In summary H K M and <.,.> n H K s the same as <.,.> n M the reprducng erne map and the Mercer erne map ead t the same RKHS, Mercer gves us an.n. bass Reprducng erne map H m K..., 1 m m ' 1 1 ', g β r :.,, Mercer erne map H M, g d 1 d T g < Φ Φ φ,, L T Γ ΦM Φ r M : 1 1 φ d : { φ.} ths turns ut t be true r any RKHS btaned rm,y 1-1 reatnshp Γ r e span φ. 1

22 Reguarzatn Q: RKHS: why d we care? A: reguarzatn we want t d we utsde the tranng set mnmzng emprca rs s nt enugh need t penaze cmpety eampe: regressn gve me n pnts, I w gve yu a mde zer errr pynma rder n-1 ncredby wggy pr generazatn

23 Reguarzatn we need t reguarze the rs R reg [ ] R [ ] + Ω [ ] emp s the unctn we are tryng t earn > 0 s the reguarzatn parameter Ω s a cmpety penazng unctna arger when s mre wggy the reguarzed rs can be usted n varus ways cnstraned mnmzatn Bayesan nerence structura rs mnmzatn... 3

24 Cnstraned mnmzatn reguarzed rs as the sutn t the prbem [ ] subect t [ ] t arg mn R Ω emp we w ta a t mre abut ths sutn s the sutn the Lagrangan prbem arg mn { R [ ] + Ω[ ]} emp s a Lagrange mutper changng s equvaent t changng t the cnstrant n the cmpety the ptma sutn 4

25 Bayesan nerence n Bayesan nerence we have ehd unctn P X ehd unctn P X prr P and search r the mamum a psterr MAP estmate arg ma P P P X { } arg ma arg ma P P P P P X X X { } [ ] { } g g arg ma arg ma P P P P X X + 5

26 Bayesan nerence P X e -Remp[] and P e -Ω[] the MAP estmate s the mnmum the reguarzed rs [ ] + Ω[ ] { R } arg mn emp the prr P assgns w prbabty t unctn wth arge Ω[] ths reects ur prr bee that t s uney that the sutn w be very cmpe eampe: Ω[] - 0,theprr s a Gaussan centered at 0 wth varance 1/ 1/ we beeve that the mst ey sutn s 0 the arger the, the mre we penaze sutns whch h are derent rm ths 6

27 Structura rs mnmzatn start rm a nested cectn ames unctns S L S 1 where S {h,, r a } r each S, nd the unctn set parameters that mnmzes the emprca rs R emp 1 mn n 1 seect the unctn cass such that R mn n L[ y, h, ] h { R + Φ } emp Φh s a unctn VC dmensn cmpety the amy S VC have shwn that ths s equvaent t mnmzng a bund n the rs, and prvdes generazatn guarantees reguarzatn wth the rght reguarzer! 7

28 The reguarzer what s a gd reguarzer? ntutn: wgger unctns have a arger nrm than smther unctns r eampe, n H K we have, c φ φ φ φ φ 8

29 The reguarzer and wth c hence, δ cc φ, φ cc φ grws wth the number c derent than zer ths s the case n whch s mre cmpe, snce t becmes a sum mre bass unctn φ dentca t what happens n Furer type decmpstns mre cecents means mre hgh-requences r ess smthness c 9

30 Reguarzatn OK, reguarzatn s a gd dea rm mutpe pnts vew prbem: mnmzng the reguarzed rs R reg [ ] R [ ] + Ω[ ] emp ver the set a unctns seems e a nghtmare t turns ut that t s nt, under sme reasnabe cndtns n the reguarzer Ω the ey s the representer therem 30

31 Representer therem Therem: Let Ω:[0, R be a strcty mntncay ncreasng unctn, H the RKHS asscated wth a erne,y L[y,] a ss unctn then, n arg mn L 1 [ ] y, + Ω [ ] admts a representatn the rm n 1.,.e. H 31

32 Pr decmpse any nt the part cntaned n the span the., and the part n the rthgna cmpement where 0 w 0 and w wth., 1 0 n + + where 0 w 0 and w wth { } { } { } 0 0 0,,., w g g w span w then { } 0, g, g [ ] + Ω Ω n [ ] Ω + Ω + Ω Ω 1., n n Ω mn. ncreasng 3 Ω + Ω 1 1.,.,

33 Pr ths shws that the secnd term n n arg mn L y, + Ω 1 s mnmzed by a unctn the stated rm. [ ] the rst, usng the reprducng prperty ,.,.,.,.,., + n 1, s aways a unctn the stated rm. hence, the mnmum must be ths rm as we 48 33

34 The pcture due t reprducng prperty: H s the sutn n H > s a mre cmpe unctn whch des nt reduce the ss hence, s the ptma sutn H e 1 e d e 3 34

35 Reevance the remarabe cnsequence the therem s that: we can reduce the mnmzatn ver the nnte dmensna space unctns t a mnmzatn n a nte dmensna space! n.,, t see ths nte that, because we have 1 T K.,,,.,, K, 35

36 Reguarzatn ths prves the wng therem Therem: Ω:[0, R s a strcty mntncay ncreasng unctn, then r any dt-prduct erne,y and ss unctn L[y,] the sutn n arg mn L 1 n [ ] y, + Ω n [ ] T K s wth., arg mn LY, K + Ω 1 1 K the Gram matr [, ] and Y...,y,... 36

37 Nte the resut that H hds r any nrm, nt ust L hwever, the eact rm the prbem w n nger be n arg mn LY T K 1 [, K ] + Ω K the argument the secnd term w have a derent rm 37

38 38

39 Prects at ths tme yu shud have a reasnabe dea what yu are gng g t d ur questns what are yu gng t d? why shud yu d t? hw are yu gng t d t? what resuts d yu epect t get? net thursday: n cass, mnute meetngs t dscuss the ur questns send me ema sayng yu are avaabe at 3:00 be prepared, n tme t spare 39

40 Pnters besdes the bs n syabus Prceedngs Neura Inrmatn Systems Prceedngs Internatna Cnerence n Machne Learnng Neura Cmputatn IEEE Transactns n Pattern Anayss and Machne Integence Jurna Machne Learnng Research IEEE Cnerence n Cmputer Vsn and Pattern Recgntn Internatna Cnerence n Cmputer Vsn IEEE Int. Cnerence e Acustcs, cs, Speech, Sgna Prcessng IEEE Internatna Cnerence Image Prcessng Internatna Jurna Cmputer Vsn As the INSPEC database and Gge 40

41 Prect tpcs msty undergrad casscatn graphcs vs natura mages n gge vsuazng data mands cmparsn LDA-based ace recgntn methds 41

Reproducing kernel Hilbert spaces. Nuno Vasconcelos ECE Department, UCSD

Reproducing kernel Hilbert spaces. Nuno Vasconcelos ECE Department, UCSD Reprucng ernel Hlbert spaces Nun Vascncels ECE Department UCSD Classfcatn a classfcatn prblem has tw tpes f varables X -vectr f bservatns features n the wrl Y - state class f the wrl Perceptrn: classfer

More information

element k Using FEM to Solve Truss Problems

element k Using FEM to Solve Truss Problems sng EM t Slve Truss Prblems A truss s an engneerng structure cmpsed straght members, a certan materal, that are tpcall pn-ned at ther ends. Such members are als called tw-rce members snce the can nl transmt

More information

The support vector machine. Nuno Vasconcelos ECE Department, UCSD

The support vector machine. Nuno Vasconcelos ECE Department, UCSD he supprt vectr machne Nun Vascncels ECE Department UCSD Outlne e have talked abut classfcatn and lnear dscrmnants then e dd a detur t talk abut kernels h d e mplement a nn-lnear bundar n the lnear dscrmnant

More information

ME2142/ME2142E Feedback Control Systems. Modelling of Physical Systems The Transfer Function

ME2142/ME2142E Feedback Control Systems. Modelling of Physical Systems The Transfer Function Mdellng Physcal Systems The Transer Functn Derental Equatns U Plant Y In the plant shwn, the nput u aects the respnse the utput y. In general, the dynamcs ths respnse can be descrbed by a derental equatn

More information

The soft-margin support vector machine. Nuno Vasconcelos ECE Department, UCSD

The soft-margin support vector machine. Nuno Vasconcelos ECE Department, UCSD he sft-margn supprt vectr machne Nun Vascncels EE Department USD lassfcatn a classfcatn prlem has t tpes f varales e.g. X - vectr f servatns features n the rld Y - state class f the rld X R fever ld pressure

More information

Exploiting vector space properties for the global optimization of process networks

Exploiting vector space properties for the global optimization of process networks Exptng vectr space prpertes fr the gbal ptmzatn f prcess netwrks Juan ab Ruz Ignac Grssmann Enterprse Wde Optmzatn Meetng March 00 Mtvatn - The ptmzatn f prcess netwrks s ne f the mst frequent prblems

More information

A Note on the Linear Programming Sensitivity. Analysis of Specification Constraints. in Blending Problems

A Note on the Linear Programming Sensitivity. Analysis of Specification Constraints. in Blending Problems Aled Mathematcal Scences, Vl. 2, 2008, n. 5, 241-248 A Nte n the Lnear Prgrammng Senstvty Analyss f Secfcatn Cnstrants n Blendng Prblems Umt Anc Callway Schl f Busness and Accuntancy Wae Frest Unversty,

More information

Support-Vector Machines

Support-Vector Machines Supprt-Vectr Machines Intrductin Supprt vectr machine is a linear machine with sme very nice prperties. Haykin chapter 6. See Alpaydin chapter 13 fr similar cntent. Nte: Part f this lecture drew material

More information

Associative Memories

Associative Memories Assocatve Memores We consder now modes for unsupervsed earnng probems, caed auto-assocaton probems. Assocaton s the task of mappng patterns to patterns. In an assocatve memory the stmuus of an ncompete

More information

A New Method for Solving Integer Linear. Programming Problems with Fuzzy Variables

A New Method for Solving Integer Linear. Programming Problems with Fuzzy Variables Appled Mathematcal Scences, Vl. 4, 00, n. 0, 997-004 A New Methd fr Slvng Integer Lnear Prgrammng Prblems wth Fuzzy Varables P. Pandan and M. Jayalakshm Department f Mathematcs, Schl f Advanced Scences,

More information

The blessing of dimensionality for kernel methods

The blessing of dimensionality for kernel methods fr kernel methds Building classifiers in high dimensinal space Pierre Dupnt Pierre.Dupnt@ucluvain.be Classifiers define decisin surfaces in sme feature space where the data is either initially represented

More information

The Support Vector Machine

The Support Vector Machine he Supprt Vectr Machne Nun Vascncels (Ken Kreutz-Delgad) UC San Deg Gemetrc Interpretatn Summarzng, the lnear dscrmnant decsn rule 0 f g> ( ) > 0 h*( ) = 1 f g ( ) < 0 has the fllng prpertes th It dvdes

More information

4DVAR, according to the name, is a four-dimensional variational method.

4DVAR, according to the name, is a four-dimensional variational method. 4D-Varatnal Data Assmlatn (4D-Var) 4DVAR, accrdng t the name, s a fur-dmensnal varatnal methd. 4D-Var s actually a smple generalzatn f 3D-Var fr bservatns that are dstrbuted n tme. he equatns are the same,

More information

Homology groups of disks with holes

Homology groups of disks with holes Hmlgy grups f disks with hles THEOREM. Let p 1,, p k } be a sequence f distinct pints in the interir unit disk D n where n 2, and suppse that fr all j the sets E j Int D n are clsed, pairwise disjint subdisks.

More information

Which Separator? Spring 1

Which Separator? Spring 1 Whch Separator? 6.034 - Sprng 1 Whch Separator? Mamze the margn to closest ponts 6.034 - Sprng Whch Separator? Mamze the margn to closest ponts 6.034 - Sprng 3 Margn of a pont " # y (w $ + b) proportonal

More information

Physical Layer: Outline

Physical Layer: Outline 18-: Intrductin t Telecmmunicatin Netwrks Lectures : Physical Layer Peter Steenkiste Spring 01 www.cs.cmu.edu/~prs/nets-ece Physical Layer: Outline Digital Representatin f Infrmatin Characterizatin f Cmmunicatin

More information

Copyright Paul Tobin 63

Copyright Paul Tobin 63 DT, Kevin t. lectric Circuit Thery DT87/ Tw-Prt netwrk parameters ummary We have seen previusly that a tw-prt netwrk has a pair f input terminals and a pair f utput terminals figure. These circuits were

More information

Wp/Lmin. Wn/Lmin 2.5V

Wp/Lmin. Wn/Lmin 2.5V UNIVERITY OF CALIFORNIA Cllege f Engneerng Department f Electrcal Engneerng and Cmputer cences Andre Vladmrescu Hmewrk #7 EEC Due Frday, Aprl 8 th, pm @ 0 Cry Prblem #.5V Wp/Lmn 0.0V Wp/Lmn n ut Wn/Lmn.5V

More information

Section 3: Detailed Solutions of Word Problems Unit 1: Solving Word Problems by Modeling with Formulas

Section 3: Detailed Solutions of Word Problems Unit 1: Solving Word Problems by Modeling with Formulas Sectn : Detaled Slutns f Wrd Prblems Unt : Slvng Wrd Prblems by Mdelng wth Frmulas Example : The factry nvce fr a mnvan shws that the dealer pad $,5 fr the vehcle. If the stcker prce f the van s $5,, hw

More information

14 The Boole/Stone algebra of sets

14 The Boole/Stone algebra of sets 14 The Ble/Stne algebra f sets 14.1. Lattces and Blean algebras. Gven a set A, the subsets f A admt the fllwng smple and famlar peratns n them: (ntersectn), (unn) and - (cmplementatn). If X, Y A, then

More information

Chapter 7. Systems 7.1 INTRODUCTION 7.2 MATHEMATICAL MODELING OF LIQUID LEVEL SYSTEMS. Steady State Flow. A. Bazoune

Chapter 7. Systems 7.1 INTRODUCTION 7.2 MATHEMATICAL MODELING OF LIQUID LEVEL SYSTEMS. Steady State Flow. A. Bazoune Chapter 7 Flud Systems and Thermal Systems 7.1 INTODUCTION A. Bazune A flud system uses ne r mre fluds t acheve ts purpse. Dampers and shck absrbers are eamples f flud systems because they depend n the

More information

Energy & Work

Energy & Work rk Dne by a Cntant Frce 6.-6.4 Energy & rk F N m jule () J rk Dne by a Cntant Frce Example Pullng a Sutcae-n-heel Fnd the wrk dne the rce 45.0-N, the angle 50.0 degree, and the dplacement 75.0 m. 3 ( F

More information

SIMULATION OF THREE PHASE THREE LEG TRANSFORMER BEHAVIOR UNDER DIFFERENT VOLTAGE SAG TYPES

SIMULATION OF THREE PHASE THREE LEG TRANSFORMER BEHAVIOR UNDER DIFFERENT VOLTAGE SAG TYPES SIMULATION OF THREE PHASE THREE LEG TRANSFORMER BEHAVIOR UNDER DIFFERENT VOLTAGE SAG TYPES Mhammadreza Dlatan Alreza Jallan Department f Electrcal Engneerng, Iran Unversty f scence & Technlgy (IUST) e-mal:

More information

Example: Suppose we want to build a classifier that recognizes WebPages of graduate students.

Example: Suppose we want to build a classifier that recognizes WebPages of graduate students. Exampe: Suppose we want to bud a cassfer that recognzes WebPages of graduate students. How can we fnd tranng data? We can browse the web and coect a sampe of WebPages of graduate students of varous unverstes.

More information

Technote 6. Op Amp Definitions. April 1990 Revised 11/22/02. Tim J. Sobering SDE Consulting

Technote 6. Op Amp Definitions. April 1990 Revised 11/22/02. Tim J. Sobering SDE Consulting Technte 6 prl 990 Resed /22/02 Op mp Dentns Tm J. Sberng SDE Cnsultng sdecnsultng@pbx.cm 990 Tm J. Sberng. ll rghts resered. Op mp Dentns Pge 2 Op mp Dentns Ths Technte summrzes the bsc pertnl mpler dentns

More information

Fall 2010 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede. (n.b. for now, we do not require that k. vectors as a k 1 matrix: ( )

Fall 2010 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede. (n.b. for now, we do not require that k. vectors as a k 1 matrix: ( ) Fall 00 Analyss f Epermental Measrements B. Esensten/rev. S. Errede Let s nvestgate the effect f a change f varables n the real & symmetrc cvarance matr aa the varance matr aa the errr matr V [ ] ( )(

More information

Feedback Principle :-

Feedback Principle :- Feedback Prncple : Feedback amplfer s that n whch a part f the utput f the basc amplfer s returned back t the nput termnal and mxed up wth the nternal nput sgnal. The sub netwrks f feedback amplfer are:

More information

AP Physics Kinematic Wrap Up

AP Physics Kinematic Wrap Up AP Physics Kinematic Wrap Up S what d yu need t knw abut this mtin in tw-dimensin stuff t get a gd scre n the ld AP Physics Test? First ff, here are the equatins that yu ll have t wrk with: v v at x x

More information

Exercises H /OOA> f Wo AJoTHS l^»-l S. m^ttrt /A/ ?C,0&L6M5 INFERENCE FOR DISTRIBUTIONS OF CATEGORICAL DATA. tts^e&n tai-ns 5 2%-cas-hews^, 27%

Exercises H /OOA> f Wo AJoTHS l^»-l S. m^ttrt /A/ ?C,0&L6M5 INFERENCE FOR DISTRIBUTIONS OF CATEGORICAL DATA. tts^e&n tai-ns 5 2%-cas-hews^, 27% /A/ mttrt?c,&l6m5 INFERENCE FOR DISTRIBUTIONS OF CATEGORICAL DATA Exercses, nuts! A cmpany clams that each batch f ttse&n ta-ns 5 2%-cas-hews, 27% almnds, 13% macadama nuts, and 8% brazl nuts. T test ths

More information

Kernel Methods for Implicit Surface Modeling

Kernel Methods for Implicit Surface Modeling Max Planck Insttut für blgsche Kybernetk Max Planck Insttute fr Blgcal Cybernetcs Techncal Reprt N. TR-125 Kernel Methds fr Implct Surface Mdelng Bernhard Schölkpf, Jachm Gesen +, Smn Spalnger + June 2004

More information

Work, Energy, and Power

Work, Energy, and Power rk, Energy, and Pwer Physics 1 There are many different TYPES f Energy. Energy is expressed in JOULES (J 419J 4.19 1 calrie Energy can be expressed mre specifically by using the term ORK( rk The Scalar

More information

Chapter 6 : Gibbs Free Energy

Chapter 6 : Gibbs Free Energy Wnter 01 Chem 54: ntrductry hermdynamcs Chapter 6 : Gbbs Free Energy... 64 Defntn f G, A... 64 Mawell Relatns... 65 Gbbs Free Energy G(,) (ure substances)... 67 Gbbs Free Energy fr Mtures... 68 ΔG f deal

More information

IAML: Support Vector Machines

IAML: Support Vector Machines 1 / 22 IAML: Supprt Vectr Machines Charles Suttn and Victr Lavrenk Schl f Infrmatics Semester 1 2 / 22 Outline Separating hyperplane with maimum margin Nn-separable training data Epanding the input int

More information

CS 3710: Visual Recognition Classification and Detection. Adriana Kovashka Department of Computer Science January 13, 2015

CS 3710: Visual Recognition Classification and Detection. Adriana Kovashka Department of Computer Science January 13, 2015 CS 3710: Vsual Recognton Classfcaton and Detecton Adrana Kovashka Department of Computer Scence January 13, 2015 Plan for Today Vsual recognton bascs part 2: Classfcaton and detecton Adrana s research

More information

Big Data Analytics! Special Topics for Computer Science CSE CSE Mar 31

Big Data Analytics! Special Topics for Computer Science CSE CSE Mar 31 Bg Data Analytcs! Specal Tpcs fr Cmputer Scence CSE 4095-001 CSE 5095-005! Mar 31 Fe Wang Asscate Prfessr Department f Cmputer Scence and Engneerng fe_wang@ucnn.edu Intrductn t Deep Learnng Perceptrn In

More information

WYSE Academic Challenge 2004 Sectional Physics Solution Set

WYSE Academic Challenge 2004 Sectional Physics Solution Set WYSE Acadec Challenge 004 Sectnal Physcs Slutn Set. Answer: e. The axu pssble statc rctn r ths stuatn wuld be: ax µ sn µ sg (0.600)(40.0N) 4.0N. Snce yur pushng rce s less than the axu pssble rctnal rce,

More information

Lyapunov Stability Stability of Equilibrium Points

Lyapunov Stability Stability of Equilibrium Points Lyapunv Stability Stability f Equilibrium Pints 1. Stability f Equilibrium Pints - Definitins In this sectin we cnsider n-th rder nnlinear time varying cntinuus time (C) systems f the frm x = f ( t, x),

More information

Support Vector Machines. Vibhav Gogate The University of Texas at dallas

Support Vector Machines. Vibhav Gogate The University of Texas at dallas Support Vector Machnes Vbhav Gogate he Unversty of exas at dallas What We have Learned So Far? 1. Decson rees. Naïve Bayes 3. Lnear Regresson 4. Logstc Regresson 5. Perceptron 6. Neural networks 7. K-Nearest

More information

NOTE ON APPELL POLYNOMIALS

NOTE ON APPELL POLYNOMIALS NOTE ON APPELL POLYNOMIALS I. M. SHEFFER An interesting characterizatin f Appell plynmials by means f a Stieltjes integral has recently been given by Thrne. 1 We prpse t give a secnd such representatin,

More information

Physics 107 HOMEWORK ASSIGNMENT #20

Physics 107 HOMEWORK ASSIGNMENT #20 Physcs 107 HOMEWORK ASSIGNMENT #0 Cutnell & Jhnsn, 7 th etn Chapter 6: Prblems 5, 7, 74, 104, 114 *5 Cncept Smulatn 6.4 prves the ptn f explrng the ray agram that apples t ths prblem. The stance between

More information

(Communicated at the meeting of January )

(Communicated at the meeting of January ) Physics. - Establishment f an Abslute Scale fr the herm-electric Frce. By G. BOR ELlUS. W. H. KEESOM. C. H. JOHANSSON and J. O. LND E. Supplement N0. 69b t the Cmmunicatins frm the Physical Labratry at

More information

Chem 204A, Fall 2004, Mid-term (II)

Chem 204A, Fall 2004, Mid-term (II) Frst tw letters f yur last name Last ame Frst ame McGll ID Chem 204A, Fall 2004, Md-term (II) Read these nstructns carefully befre yu start tal me: 2 hurs 50 mnutes (6:05 PM 8:55 PM) 1. hs exam has ttal

More information

Conservation of Energy

Conservation of Energy Cnservatn f Energy Equpment DataStud, ruler 2 meters lng, 6 n ruler, heavy duty bench clamp at crner f lab bench, 90 cm rd clamped vertcally t bench clamp, 2 duble clamps, 40 cm rd clamped hrzntally t

More information

Design of Analog Integrated Circuits

Design of Analog Integrated Circuits Desgn f Analg Integrated Crcuts I. Amplfers Desgn f Analg Integrated Crcuts Fall 2012, Dr. Guxng Wang 1 Oerew Basc MOS amplfer structures Cmmn-Surce Amplfer Surce Fllwer Cmmn-Gate Amplfer Desgn f Analg

More information

A Neural Network Based Spectrum Prediction Scheme for Cognitive Radio

A Neural Network Based Spectrum Prediction Scheme for Cognitive Radio A Neura Netwrk Based Spectrum Predctn Scheme fr Cgntve Rad Vams Krshna Tumuuru, Png Wang and Dust Nyat Center fr Mutmeda and Netwrk Techngy (CeMNeT) Sch f Cmputer Engneerng, Nanyang Techngca Unversty,

More information

, where. This is a highpass filter. The frequency response is the same as that for P.P.14.1 RC. Thus, the sketches of H and φ are shown below.

, where. This is a highpass filter. The frequency response is the same as that for P.P.14.1 RC. Thus, the sketches of H and φ are shown below. hapter 4, Slutn. H ( H(, where H π H ( φ H ( tan - ( Th a hghpa lter. The requency repne the ame a that r P.P.4. except that. Thu, the ketche H and φ are hwn belw. H.77 / φ 9 45 / hapter 4, Slutn. H(,

More information

ENGI 4421 Probability & Statistics

ENGI 4421 Probability & Statistics Lecture Ntes fr ENGI 441 Prbablty & Statstcs by Dr. G.H. Gerge Asscate Prfessr, Faculty f Engneerng and Appled Scence Seventh Edtn, reprnted 018 Sprng http://www.engr.mun.ca/~ggerge/441/ Table f Cntents

More information

CONVEX COMBINATIONS OF ANALYTIC FUNCTIONS

CONVEX COMBINATIONS OF ANALYTIC FUNCTIONS rnat. J. Math. & Math. S. Vl. 6 N. (983) 33534 335 ON THE RADUS OF UNVALENCE OF CONVEX COMBNATONS OF ANALYTC FUNCTONS KHALDA. NOOR, FATMA M. ALOBOUD and NAEELA ALDHAN Mathematcs Department Scence Cllege

More information

Approach: (Equilibrium) TD analysis, i.e., conservation eqns., state equations Issues: how to deal with

Approach: (Equilibrium) TD analysis, i.e., conservation eqns., state equations Issues: how to deal with Schl f Aerspace Chemcal D: Mtvatn Prevus D Analyss cnsdered systems where cmpstn f flud was frzen fxed chemcal cmpstn Chemcally eactng Flw but there are numerus stuatns n prpulsn systems where chemcal

More information

Interference is when two (or more) sets of waves meet and combine to produce a new pattern.

Interference is when two (or more) sets of waves meet and combine to produce a new pattern. Interference Interference is when tw (r mre) sets f waves meet and cmbine t prduce a new pattern. This pattern can vary depending n the riginal wave directin, wavelength, amplitude, etc. The tw mst extreme

More information

Faculty of Engineering

Faculty of Engineering Faculty f Engneerng DEPARTMENT f ELECTRICAL AND ELECTRONIC ENGINEERING EEE 223 Crcut Thery I Instructrs: M. K. Uygurğlu E. Erdl Fnal EXAMINATION June 20, 2003 Duratn : 120 mnutes Number f Prblems: 6 Gd

More information

The Gaussian classifier. Nuno Vasconcelos ECE Department, UCSD

The Gaussian classifier. Nuno Vasconcelos ECE Department, UCSD he Gaussan classfer Nuno Vasconcelos ECE Department, UCSD Bayesan decson theory recall that we have state of the world X observatons g decson functon L[g,y] loss of predctng y wth g Bayes decson rule s

More information

1 The limitations of Hartree Fock approximation

1 The limitations of Hartree Fock approximation Chapter: Pst-Hartree Fck Methds - I The limitatins f Hartree Fck apprximatin The n electrn single determinant Hartree Fck wave functin is the variatinal best amng all pssible n electrn single determinants

More information

Admissibility Conditions and Asymptotic Behavior of Strongly Regular Graphs

Admissibility Conditions and Asymptotic Behavior of Strongly Regular Graphs Admissibility Cnditins and Asympttic Behavir f Strngly Regular Graphs VASCO MOÇO MANO Department f Mathematics University f Prt Oprt PORTUGAL vascmcman@gmailcm LUÍS ANTÓNIO DE ALMEIDA VIEIRA Department

More information

Flipping Physics Lecture Notes: Simple Harmonic Motion Introduction via a Horizontal Mass-Spring System

Flipping Physics Lecture Notes: Simple Harmonic Motion Introduction via a Horizontal Mass-Spring System Flipping Physics Lecture Ntes: Simple Harmnic Mtin Intrductin via a Hrizntal Mass-Spring System A Hrizntal Mass-Spring System is where a mass is attached t a spring, riented hrizntally, and then placed

More information

A Note on Equivalences in Measuring Returns to Scale

A Note on Equivalences in Measuring Returns to Scale Internatnal Jurnal f Busness and Ecnmcs, 2013, Vl. 12, N. 1, 85-89 A Nte n Equvalences n Measurng Returns t Scale Valentn Zelenuk Schl f Ecnmcs and Centre fr Effcenc and Prductvt Analss, The Unverst f

More information

Equilibrium of Stress

Equilibrium of Stress Equilibrium f Stress Cnsider tw perpendicular planes passing thrugh a pint p. The stress cmpnents acting n these planes are as shwn in ig. 3.4.1a. These stresses are usuall shwn tgether acting n a small

More information

CHAPTER 6 WORK AND ENERGY

CHAPTER 6 WORK AND ENERGY CHAPTER 6 WORK AND ENERGY CONCEPTUAL QUESTIONS 16. REASONING AND SOLUTION A trapeze artist, starting rm rest, swings dwnward n the bar, lets g at the bttm the swing, and alls reely t the net. An assistant,

More information

READING STATECHART DIAGRAMS

READING STATECHART DIAGRAMS READING STATECHART DIAGRAMS Figure 4.48 A Statechart diagram with events The diagram in Figure 4.48 shws all states that the bject plane can be in during the curse f its life. Furthermre, it shws the pssible

More information

Thermodynamics Partial Outline of Topics

Thermodynamics Partial Outline of Topics Thermdynamics Partial Outline f Tpics I. The secnd law f thermdynamics addresses the issue f spntaneity and invlves a functin called entrpy (S): If a prcess is spntaneus, then Suniverse > 0 (2 nd Law!)

More information

EE 204 Lecture 25 More Examples on Power Factor and the Reactive Power

EE 204 Lecture 25 More Examples on Power Factor and the Reactive Power EE 204 Lecture 25 Mre Examples n Pwer Factr and the Reactve Pwer The pwer factr has been defned n the prevus lecture wth an example n pwer factr calculatn. We present tw mre examples n ths lecture. Example

More information

Perturbation approach applied to the asymptotic study of random operators.

Perturbation approach applied to the asymptotic study of random operators. Perturbatin apprach applied t the asympttic study f rm peratrs. André MAS, udvic MENNETEAU y Abstract We prve that, fr the main mdes f stchastic cnvergence (law f large numbers, CT, deviatins principles,

More information

Sections 15.1 to 15.12, 16.1 and 16.2 of the textbook (Robbins-Miller) cover the materials required for this topic.

Sections 15.1 to 15.12, 16.1 and 16.2 of the textbook (Robbins-Miller) cover the materials required for this topic. Tpic : AC Fundamentals, Sinusidal Wavefrm, and Phasrs Sectins 5. t 5., 6. and 6. f the textbk (Rbbins-Miller) cver the materials required fr this tpic.. Wavefrms in electrical systems are current r vltage

More information

Relationships Between Frequency, Capacitance, Inductance and Reactance.

Relationships Between Frequency, Capacitance, Inductance and Reactance. P Physics Relatinships between f,, and. Relatinships Between Frequency, apacitance, nductance and Reactance. Purpse: T experimentally verify the relatinships between f, and. The data cllected will lead

More information

Lecture 13: Markov Chain Monte Carlo. Gibbs sampling

Lecture 13: Markov Chain Monte Carlo. Gibbs sampling Lecture 13: Markv hain Mnte arl Gibbs sampling Gibbs sampling Markv chains 1 Recall: Apprximate inference using samples Main idea: we generate samples frm ur Bayes net, then cmpute prbabilities using (weighted)

More information

Public Key Cryptography. Tim van der Horst & Kent Seamons

Public Key Cryptography. Tim van der Horst & Kent Seamons Public Key Cryptgraphy Tim van der Hrst & Kent Seamns Last Updated: Oct 5, 2017 Asymmetric Encryptin Why Public Key Crypt is Cl Has a linear slutin t the key distributin prblem Symmetric crypt has an expnential

More information

Shell Stiffness for Diffe ent Modes

Shell Stiffness for Diffe ent Modes Engneerng Mem N 28 February 0 979 SUGGESTONS FOR THE DEFORMABLE SUBREFLECTOR Sebastan vn Herner Observatns wth the present expermental versn (Engneerng Dv nternal Reprt 09 July 978) have shwn that a defrmable

More information

FINITE BOOLEAN ALGEBRA. 1. Deconstructing Boolean algebras with atoms. Let B = <B,,,,,0,1> be a Boolean algebra and c B.

FINITE BOOLEAN ALGEBRA. 1. Deconstructing Boolean algebras with atoms. Let B = <B,,,,,0,1> be a Boolean algebra and c B. FINITE BOOLEAN ALGEBRA 1. Decnstructing Blean algebras with atms. Let B = be a Blean algebra and c B. The ideal generated by c, (c], is: (c] = {b B: b c} The filter generated by c, [c), is:

More information

10-701/ Machine Learning, Fall 2005 Homework 3

10-701/ Machine Learning, Fall 2005 Homework 3 10-701/15-781 Machne Learnng, Fall 2005 Homework 3 Out: 10/20/05 Due: begnnng of the class 11/01/05 Instructons Contact questons-10701@autonlaborg for queston Problem 1 Regresson and Cross-valdaton [40

More information

Flipping Physics Lecture Notes: Simple Harmonic Motion Introduction via a Horizontal Mass-Spring System

Flipping Physics Lecture Notes: Simple Harmonic Motion Introduction via a Horizontal Mass-Spring System Flipping Physics Lecture Ntes: Simple Harmnic Mtin Intrductin via a Hrizntal Mass-Spring System A Hrizntal Mass-Spring System is where a mass is attached t a spring, riented hrizntally, and then placed

More information

Circuits Op-Amp. Interaction of Circuit Elements. Quick Check How does closing the switch affect V o and I o?

Circuits Op-Amp. Interaction of Circuit Elements. Quick Check How does closing the switch affect V o and I o? Crcuts Op-Amp ENGG1015 1 st Semester, 01 Interactn f Crcut Elements Crcut desgn s cmplcated by nteractns amng the elements. Addng an element changes vltages & currents thrughut crcut. Example: clsng a

More information

IGEE 401 Power Electronic Systems. Solution to Midterm Examination Fall 2004

IGEE 401 Power Electronic Systems. Solution to Midterm Examination Fall 2004 Jós, G GEE 401 wer Electrnc Systems Slutn t Mdterm Examnatn Fall 2004 Specal nstructns: - Duratn: 75 mnutes. - Materal allwed: a crb sheet (duble sded 8.5 x 11), calculatr. - Attempt all questns. Make

More information

Physics 212. Lecture 12. Today's Concept: Magnetic Force on moving charges. Physics 212 Lecture 12, Slide 1

Physics 212. Lecture 12. Today's Concept: Magnetic Force on moving charges. Physics 212 Lecture 12, Slide 1 Physics 1 Lecture 1 Tday's Cncept: Magnetic Frce n mving charges F qv Physics 1 Lecture 1, Slide 1 Music Wh is the Artist? A) The Meters ) The Neville rthers C) Trmbne Shrty D) Michael Franti E) Radiatrs

More information

x x

x x Mdeling the Dynamics f Life: Calculus and Prbability fr Life Scientists Frederick R. Adler cfrederick R. Adler, Department f Mathematics and Department f Bilgy, University f Utah, Salt Lake City, Utah

More information

V. Electrostatics Lecture 27a: Diffuse charge at electrodes

V. Electrostatics Lecture 27a: Diffuse charge at electrodes V. Electrstatcs Lecture 27a: Dffuse charge at electrdes Ntes by MIT tudent We have talked abut the electrc duble structures and crrespndng mdels descrbng the n and ptental dstrbutn n the duble layer. Nw

More information

and the Doppler frequency rate f R , can be related to the coefficients of this polynomial. The relationships are:

and the Doppler frequency rate f R , can be related to the coefficients of this polynomial. The relationships are: Algrithm fr Estimating R and R - (David Sandwell, SIO, August 4, 2006) Azimith cmpressin invlves the alignment f successive eches t be fcused n a pint target Let s be the slw time alng the satellite track

More information

COMP 551 Applied Machine Learning Lecture 9: Support Vector Machines (cont d)

COMP 551 Applied Machine Learning Lecture 9: Support Vector Machines (cont d) COMP 551 Applied Machine Learning Lecture 9: Supprt Vectr Machines (cnt d) Instructr: Herke van Hf (herke.vanhf@mail.mcgill.ca) Slides mstly by: Class web page: www.cs.mcgill.ca/~hvanh2/cmp551 Unless therwise

More information

f t(y)dy f h(x)g(xy) dx fk 4 a. «..

f t(y)dy f h(x)g(xy) dx fk 4 a. «.. CERTAIN OPERATORS AND FOURIER TRANSFORMS ON L2 RICHARD R. GOLDBERG 1. Intrductin. A well knwn therem f Titchmarsh [2] states that if fel2(0, ) and if g is the Furier csine transfrm f/, then G(x)=x~1Jx0g(y)dy

More information

PHYSICS 536 Experiment 12: Applications of the Golden Rules for Negative Feedback

PHYSICS 536 Experiment 12: Applications of the Golden Rules for Negative Feedback PHYSICS 536 Experment : Applcatns f the Glden Rules fr Negatve Feedback The purpse f ths experment s t llustrate the glden rules f negatve feedback fr a varety f crcuts. These cncepts permt yu t create

More information

CS 477/677 Analysis of Algorithms Fall 2007 Dr. George Bebis Course Project Due Date: 11/29/2007

CS 477/677 Analysis of Algorithms Fall 2007 Dr. George Bebis Course Project Due Date: 11/29/2007 CS 477/677 Analysis f Algrithms Fall 2007 Dr. Gerge Bebis Curse Prject Due Date: 11/29/2007 Part1: Cmparisn f Srting Algrithms (70% f the prject grade) The bjective f the first part f the assignment is

More information

PT326 PROCESS TRAINER

PT326 PROCESS TRAINER PT326 PROCESS TRAINER 1. Descrptn f the Apparatus PT 326 Prcess Traner The PT 326 Prcess Traner mdels cmmn ndustral stuatns n whch temperature cntrl s requred n the presence f transprt delays and transfer

More information

This section is primarily focused on tools to aid us in finding roots/zeros/ -intercepts of polynomials. Essentially, our focus turns to solving.

This section is primarily focused on tools to aid us in finding roots/zeros/ -intercepts of polynomials. Essentially, our focus turns to solving. Sectin 3.2: Many f yu WILL need t watch the crrespnding vides fr this sectin n MyOpenMath! This sectin is primarily fcused n tls t aid us in finding rts/zers/ -intercepts f plynmials. Essentially, ur fcus

More information

LCAO APPROXIMATIONS OF ORGANIC Pi MO SYSTEMS The allyl system (cation, anion or radical).

LCAO APPROXIMATIONS OF ORGANIC Pi MO SYSTEMS The allyl system (cation, anion or radical). Principles f Organic Chemistry lecture 5, page LCAO APPROIMATIONS OF ORGANIC Pi MO SYSTEMS The allyl system (catin, anin r radical).. Draw mlecule and set up determinant. 2 3 0 3 C C 2 = 0 C 2 3 0 = -

More information

ANALOG ELECTRONICS 1 DR NORLAILI MOHD NOH

ANALOG ELECTRONICS 1 DR NORLAILI MOHD NOH 24 ANALOG LTRONIS TUTORIAL DR NORLAILI MOHD NOH . 0 8kΩ Gen, Y β β 00 T F 26, 00 0.7 (a)deterne the dc ltages at the 3 X ternals f the JT (,, ). 0kΩ Z (b) Deterne g,r π and r? (c) Deterne the ltage gan

More information

CIRCUIT ANALYSIS II Chapter 1 Sinusoidal Alternating Waveforms and Phasor Concept. Sinusoidal Alternating Waveforms and

CIRCUIT ANALYSIS II Chapter 1 Sinusoidal Alternating Waveforms and Phasor Concept. Sinusoidal Alternating Waveforms and U ANAYSS hapter Snusdal Alternatng Wavefrs and Phasr ncept Snusdal Alternatng Wavefrs and Phasr ncept ONNS. Snusdal Alternatng Wavefrs.. General Frat fr the Snusdal ltage & urrent.. Average alue..3 ffectve

More information

36.1 Why is it important to be able to find roots to systems of equations? Up to this point, we have discussed how to find the solution to

36.1 Why is it important to be able to find roots to systems of equations? Up to this point, we have discussed how to find the solution to ChE Lecture Notes - D. Keer, 5/9/98 Lecture 6,7,8 - Rootndng n systems o equatons (A) Theory (B) Problems (C) MATLAB Applcatons Tet: Supplementary notes rom Instructor 6. Why s t mportant to be able to

More information

Note 2. Ling fong Li. 1 Klein Gordon Equation Probablity interpretation Solutions to Klein-Gordon Equation... 2

Note 2. Ling fong Li. 1 Klein Gordon Equation Probablity interpretation Solutions to Klein-Gordon Equation... 2 Note 2 Lng fong L Contents Ken Gordon Equaton. Probabty nterpretaton......................................2 Soutons to Ken-Gordon Equaton............................... 2 2 Drac Equaton 3 2. Probabty nterpretaton.....................................

More information

Distributions, spatial statistics and a Bayesian perspective

Distributions, spatial statistics and a Bayesian perspective Distributins, spatial statistics and a Bayesian perspective Dug Nychka Natinal Center fr Atmspheric Research Distributins and densities Cnditinal distributins and Bayes Thm Bivariate nrmal Spatial statistics

More information

ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE

ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE School of Computer and Communcaton Scences Handout 0 Prncples of Dgtal Communcatons Solutons to Problem Set 4 Mar. 6, 08 Soluton. If H = 0, we have Y = Z Z = Y

More information

MARKOV CHAIN AND HIDDEN MARKOV MODEL

MARKOV CHAIN AND HIDDEN MARKOV MODEL MARKOV CHAIN AND HIDDEN MARKOV MODEL JIAN ZHANG JIANZHAN@STAT.PURDUE.EDU Markov chan and hdden Markov mode are probaby the smpest modes whch can be used to mode sequenta data,.e. data sampes whch are not

More information

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification E395 - Pattern Recognton Solutons to Introducton to Pattern Recognton, Chapter : Bayesan pattern classfcaton Preface Ths document s a soluton manual for selected exercses from Introducton to Pattern Recognton

More information

Cambridge Assessment International Education Cambridge Ordinary Level. Published

Cambridge Assessment International Education Cambridge Ordinary Level. Published Cambridge Assessment Internatinal Educatin Cambridge Ordinary Level ADDITIONAL MATHEMATICS 4037/1 Paper 1 Octber/Nvember 017 MARK SCHEME Maximum Mark: 80 Published This mark scheme is published as an aid

More information

Generalized Linear Methods

Generalized Linear Methods Generalzed Lnear Methods 1 Introducton In the Ensemble Methods the general dea s that usng a combnaton of several weak learner one could make a better learner. More formally, assume that we have a set

More information

Solving the VAR Sources Planning with Coupling Investment Capacity Constrains Using Genetic Algorithm

Solving the VAR Sources Planning with Coupling Investment Capacity Constrains Using Genetic Algorithm Svng the VAR Surces Pannng wth Cupng Investment Capacty Cnstrans Usng Genetc Agrthm Ch -Hsn Ln 1, Shn-Yeu Ln 2 Dep. f Eectrnc Engneerng, Ka Yuan Insttute f Techngy, Tawan 1 Dept. f Eectrca and Cntr Engneerng,

More information

Integrating Certified Lengths to Strengthen Metrology Network Uncertainty

Integrating Certified Lengths to Strengthen Metrology Network Uncertainty Integratng Certfed engths t Strengthen Metrlgy Netwrk Uncertanty Authrs: Jseph Calkns, PhD New Rver Knematcs je@knematcs.cm Sctt Sandwth New Rver Knematcs sctt@knematcs.cm Abstract Calbrated and traceable

More information

, which yields. where z1. and z2

, which yields. where z1. and z2 The Gaussian r Nrmal PDF, Page 1 The Gaussian r Nrmal Prbability Density Functin Authr: Jhn M Cimbala, Penn State University Latest revisin: 11 September 13 The Gaussian r Nrmal Prbability Density Functin

More information

ECE 2100 Circuit Analysis

ECE 2100 Circuit Analysis ECE 2100 Circuit Analysis Lessn 25 Chapter 9 & App B: Passive circuit elements in the phasr representatin Daniel M. Litynski, Ph.D. http://hmepages.wmich.edu/~dlitynsk/ ECE 2100 Circuit Analysis Lessn

More information

In SMV I. IAML: Support Vector Machines II. This Time. The SVM optimization problem. We saw:

In SMV I. IAML: Support Vector Machines II. This Time. The SVM optimization problem. We saw: In SMV I IAML: Supprt Vectr Machines II Nigel Gddard Schl f Infrmatics Semester 1 We sa: Ma margin trick Gemetry f the margin and h t cmpute it Finding the ma margin hyperplane using a cnstrained ptimizatin

More information

Lecture 2: Supervised vs. unsupervised learning, bias-variance tradeoff

Lecture 2: Supervised vs. unsupervised learning, bias-variance tradeoff Lecture 2: Supervised vs. unsupervised learning, bias-variance tradeff Reading: Chapter 2 STATS 202: Data mining and analysis September 27, 2017 1 / 20 Supervised vs. unsupervised learning In unsupervised

More information