Machine Learning. Hopfield networks. Prof. Dr. Volker Sperschneider
|
|
- Ronald Fleming
- 6 years ago
- Views:
Transcription
1 Mache Learg Hopfed eor Prof. Dr. Voer Spercheder AG Machee Lere ud Naürchprachche Seme Iu für Iforma Techche Fauä Aber-Ludg-Uverä Freburg
2 Hopfed eor I. Movao II. Bac defo III. Free eerg IV. Hebb earg V. Correao earg VI. Sovg combaora opmao probem b free eerg mmao
3 Hopfed eor I. Movao
4 Movao MLP ove a a mag a deco corog a devce recogg he face of gradmoher Compe formao provded o he e (vecor of feaure vaue, vecor of meaureme, o of pe) ad o a ma amou of formao devered b he e (e/o, coro vaue, recoged/o recoged)
5 Movao Wha abou he a of memorg he face of gradmoher - ad o of furher mage? o mp a a daa-bae h equea acce bu h pob of recoverage from a hgh dored mage or a para mage or from voce of gradmoher peae cogve paube maer Hebb earg rue appcao oud be ce
6 Movao Such or of memor caed aocave memor. Memor he core of hgher cogve fuco: cocoue, ef-cocoue, aguage, oca behavour, I bra aumed o be ocaed hgher corca rego of mave ercoeced euro, bu o aered rego Here a appcao eampe:
7 Ma-Pac Magdeburg
8 Eampe of a Hopfed eor (hrehod dcaed de he euro)
9 Reaed o Igmode of phc magec dpoe each dpoe eperece magec force from he oher
10 Hopfed eor II. Bac defo
11 bar euro compee ercoeced mmerc egh ero ef-acvao Threhod bar ae epu of euro ae a uua
12 mmerc egh ero ef acvao 0 hrehod θ ( ) { } ae epu 0, e ( ) θ
13 Sae rao Leg euro fre ae mea o repace b vaue. ( ) Leg euro ge mue ae mea o repace b vaue 0. ( 0)
14 Acve ad acve euro Neuro acve ae f ( e ( ) 0 0) ( e ( ) < 0 > )
15 Acve ad acve euro Neuro acve ae f ( e ( ) 0 0) ( e ( ) < 0 > ) Neuro acve ae f ( e ( ) < 0 0) e ( ) 0 ( e ( ) > 0 )
16 Acve ad acve euro Neuro acve ae f ( e ( ) 0 0) ( e ( ) < 0 > ) Neuro acve ae f ( e ( ) < 0 0) e ( ) 0 ( e ( ) > 0 ) Sae abe f ever euro acve
17 Damc of a Hopfed eor
18 Damc of a Hopfed eor Achroou damc: WHILE acua ae o abe DO eec a acve euro ad e fre/go mue END
19 Damc of a Hopfed eor Schroou damc: WHILE acua ae o abe DO e a acve euro fre/go mue parae END
20 Schroou damc more reguar ha achroou oe, bu ee ha ma happe: - ½ - ½ - euro havg hrehod -½, egh - ae (0,0) che parae o ae (,) ae (,) che parae o ae (0,0) Iead of covergg o a abe ae he e che forever beee o ae: -cce
21 Schroou damc o cogve adequae: o bra chroaor o Schroou damc eher coverge o a abe ae or ru o a -cce: proof o foo Achroou damc aa coverge o a abe ae: proof o foo, ue cocep of free eerg
22 Sabe ae or -cce chroou damc Ne h chroou damc muaed b a e ogeher h a cop of he e ad egh a ho he eampe:
23 Le ae rao he chroou e be () () (3) The fr chroou ep muaed b 3 ( he eampe) achroou ep o he cop e: forh The ecod chroou ep muaed b 3 ( he eampe) achroou ep o he orga e: bac ad o o
24 The he achroou e ae rao ca be geeraed a foo: ()() bac (3)() forh (3)(4)... Sabe ae be arrved eher oe of he foog a: Thu () (). ()() ()() ()() ()()
25 If furhermore () () () e oba a abe ae. Ohere e oba a -cce
26 Hopfed eor III. Free eerg
27 Free eerg of a ae ae ( ) E( ) θ vecor oao : E( ) T T θ
28 Ver ma eampe (a hrehod 0)
29 Sae, free eerge, e pu, acve euro eo
30 ae rao
31 eerg dece - euro fre ae acve euro 0 e () > 0 che o ae for a
32 E E ) ( ) ( θ θ
33 E E ) ( ) ( θ θ θ θ
34 E E ) ( ) ( θ θ θ θ θ
35 E E ) ( ) ( θ θ θ θ θ θ
36 ) ( ) ( ) ( ) ( > e e E E θ θ θ θ θ θ θ
37 eerg dece - euro ge mue ae acve euro e () < 0 che o ae 0 for a Eerce: Sho ha E() - E() - e () >
38 Theorem: Uder achroou damc a Hopfed e aa ru o abe ae. Proof: I ever ep eerg of he acua ae rc decreae. A eerg fuco adop o fe ma vaue (gve a emao of ho ma) eerg dece mu fa come o a ed
39 Eerce: Adap a oo for bar Hopfed e o he cae of bpoar euro. Aume ae raformed o ae b he aco of euro. Sho ha E( ) E( ) e ( ) e ( ) f euro fre, ad f euro ge mue. E( ) E( ) e ( ) e ( )
40 Coruc a eampe of a bpoar Hopfed eor hch ru o a -cce uder chroou damc
41 Hopfed eor IV. Hebb earg
42 Hebb earg We ue a Hopfed eor h euro. Neuro are bpoar (, -) euro. Threhod are e o ero. We coder a rag e of mage. To mage (ha, bpoar ae) ad are caed orhogoa f her er produc ero: T
43 Cae pe 00 radom e pe 00 radom e pe
44 Hebb earg Trag e {,, } {, } euro,,, Threhod 0 Hebb egh
45 Sab of orhogoa vecor Aume orhogoa of rag vecor: ( p ) T q 0 for p q The for a,, ad,, : e ( ) Proof:
46 e ) (
47 e ) ( ) (
48 e ) ( ) (
49 e ) ( ) (
50 T e ) ) (( ) ( ) (
51 T e ) (0 ) ) (( ) ( ) (
52 T e ) (0 ) ) (( ) ( ) (
53 Sorage capac uder Hebb earg Hopfed e h euro ca ore ad afe reca a mo 3,8% ae uder Hebb rue. Thu he cae e houd be abe o afe ore o more ha 80 mage. Ug a dffere earg rue (correao earg) he auhor a abe o ore more ha 000 mage afe eve h 50% oe
54 Coder euro huma core. Though hee euro are b o mea compee coeced e u u for fu emae ho og oe coud ore mage per ecod, 0 hour a da, aumg he 0,38 emao: , ear Loer coecv (0.000 per euro) mgh be compeaed b more cever earg rue
55 Hebb earg dffer omeho for bar euro ad bpoar euro: euro euro egh updae O muaou acve euro ead o roger egh: orga Hebb rue
56 Hopfed eor V. Correao earg
57 Correao-baed earg Correao.r.. a ae rag e Meaure of ho e ha amog a ae rag e euro have he ame acvao κ
58 Correao earg Correao.r.. o a abe ae of acua e h egh vecor Meaure of ho e ha amog he abe ae euro have he ame acvao: κ ( ) abe. r.. S umber of a abe ae S
59 κ epree ho rog euro acvao are correaed h rag ae: ac, dered correao κ - epree ho rog euro acvao are correaed h abe ae of he acua e: damc, de faco correao
60 If for euro ad de faco correao maer (greaer) ha dered correao h ca be correced b creag (decreag) he egh beee ad. I a mpemeao egave correao are emaed b ampg a uffce umber of abe ae (ead of compug a)
61 Correao earg rue η( )( κ κ ( )) Learg rae η() decreae (a uua) h me. Paub of he earg rue be eabhed he e chaper o Boma mache
62 Hopfed eor VI. Sovg combaora opmao probem b free eerg mmao
63 Ug Hopfed eor for he ouo of combaora opmao probem A Hopfed e mme free eerg. Sabe ae are oca mma of free eerg fuco uder Hammg dace of ae (Eerce). Free eerg a quadrac fuco. Quadrac fuco are que epreve
64 Traveg aema probem (TSP) Le ode be gve:,,., Le a dace mar h dace d beee a o ode ad be gve. Aume d d > 0 d 0 Fd a our hrough a ode, ha a permu- ao π of a ode, h mma our co d π ( ) π ( ) dπ ( ) π ()
65 TSP repreeed b a Hopfed e Ue a Hopfed e h ma bar euro ha are arraged a quare mar. Neuro ro ad coum refered b N. Tour π repreeed b ae a foo: π ( )
66 A eampe h 6 ode ma hep carfg ho our π are repreeed b ae: π( ) I he repreeg ae euro h vaue are ho bac
67 Eracg dace co from ae Le our π be repreeed b ae. The for < d a b d π ( ) π ( ) ab ab a b a, b Wh? Sce o a π() gve a corbuo o he ouer um, ad o b π() gve a corbuo o he er um. d ab
68 The ame hod for he fa ep bac o he ar ode: d a b d π ( ) π () ab ab a b a, b Thu TSP am o mme ab d ab ab a, b a, b over a ae ha repree our. d ab d ab
69 No ever ae repree a our O ae ha coa eac oe vaue ever ro ad eac oe vaue ever coum repree our. Th ca be repreeed b a e of cora: a b b a ab ab
70 Th ca be epreed b he mmao of he foog quadrac fuco: ( a b ( a b (( ab ab ) ) a b ab ) ( a b b b a ( ( ba ab ba ) ) ) )
71 Boh quadrac fuco (our co fuco ad our admb cora fuco) are combed a commo fuco: a, b C a b d (( ab a, b b ab ) ( a b b a d ab ba ) ) Coa C > 0 coro heher mma of our co or admb of our cou more
72 Noe ha a oca mmum of h commo quadrac fuco he acheved ae mu o ecear be a ae repreeg a our. Eve f ae repreeed a our mu o be a oca or eve goba mmum
73 Commo quadrac fuco erpreed a free eerg of a Hopfed e A euro of he ued Hopfed e are ed b a doube de N ab e have o dea h egh havg 4 dce ad hrehod havg dce. ( ab)( cd ) ( ab) Thu free eerg formua read a foo: E( ) θ ad ab ( ab)( cd ) cd a, b, c, d a, b θ ab ( ab)
74 We raform commo quadrac fuco o free eerg form: a, b C a, b C a b (( b a, b ab ) ( a b b a d d ab ab a, b a a b b d d ) ( abac ab baca a b, c b b, c b ab ab ba ) ba )
75 a, b a b d ab a, b b C aac Cab 4 a,, c, a, b a, b a d ab C ab C For mmao coa erm C doe o pa a roe, hu e deee. a, b a b d ab a, b b C aac Cab 4 a,, c, a, b a, b a d ab C ab
76 a, b ab a b C aac Cab 4 a,, c, a, b a, b a b d d ab ab a, b ab a a b b d d ab ab C C ab a a,, a C a C aac Cab 4 a,, c, a, b a a, C a
77 No mach obaed quadrac form o free eerg: ab a b d ab ab a b d ab C a a,, a C a C aac Cab 4 a,, c, a, b a a, C a E( ) θ ab ( ab)( cd ) cd a, b, c, d a, b ab ( ab)
78 ( a)( b ) ( b )( a) d ab < a b a ( a)( b) ( b)( a) d ab a b a ( a)( ac) ( ac)( a) C a c ( a)( b) ( b)( a) C a b a A oher egh are e o ero. θ ( ab) 6C a b
Reliability Equivalence of a Parallel System with Non-Identical Components
Ieraoa Mahemaca Forum 3 8 o. 34 693-7 Reaby Equvaece of a Parae Syem wh No-Ideca ompoe M. Moaer ad mmar M. Sarha Deparme of Sac & O.R. oege of Scece Kg Saud Uvery P.O.ox 455 Ryadh 45 Saud raba aarha@yahoo.com
More informationLecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination
Lecure 3 Topc : Drbuo, hypohe eg, ad ample ze deermao The Sude - drbuo Coder a repeaed drawg of ample of ze from a ormal drbuo of mea. For each ample, compue,,, ad aoher ac,, where: The ac he devao of
More informationEfficient Estimators for Population Variance using Auxiliary Information
Global Joural of Mahemacal cece: Theor ad Praccal. IN 97-3 Volume 3, Number (), pp. 39-37 Ieraoal Reearch Publcao Houe hp://www.rphoue.com Effce Emaor for Populao Varace ug Aular Iformao ubhah Kumar Yadav
More informationCS344: Introduction to Artificial Intelligence
C344: Iroduco o Arfcal Iellgece Puhpa Bhaacharyya CE Dep. IIT Bombay Lecure 3 3 32 33: Forward ad bacward; Baum elch 9 h ad 2 March ad 2 d Aprl 203 Lecure 27 28 29 were o EM; dae 2 h March o 8 h March
More informationNational Conference on Recent Trends in Synthesis and Characterization of Futuristic Material in Science for the Development of Society
ABSTRACT Naoa Coferece o Rece Tred Syhe ad Characerzao of Fuurc Maera Scece for he Deveome of Socey (NCRDAMDS-208) I aocao wh Ieraoa Joura of Scefc Reearch Scece ad Techoogy Some New Iegra Reao of I- Fuco
More informationParameters Estimation in a General Failure Rate Semi-Markov Reliability Model
Joura of Saca Theory ad Appcao Vo. No. (Sepember ) - Parameer Emao a Geera Faure Rae Sem-Marov Reaby Mode M. Fahzadeh ad K. Khorhda Deparme of Sac Facuy of Mahemaca Scece Va-e-Ar Uvery of Rafaja Rafaja
More informationSome Improved Estimators for Population Variance Using Two Auxiliary Variables in Double Sampling
Vplav Kumar gh Rajeh gh Deparme of ac Baara Hdu Uver Varaa-00 Ida Flore maradache Uver of ew Meco Gallup UA ome Improved Emaor for Populao Varace Ug Two Aular Varable Double amplg Publhed : Rajeh gh Flore
More informationThe Signal, Variable System, and Transformation: A Personal Perspective
The Sgal Varable Syem ad Traformao: A Peroal Perpecve Sherv Erfa 35 Eex Hall Faculy of Egeerg Oule Of he Talk Iroduco Mahemacal Repreeao of yem Operaor Calculu Traformao Obervao O Laplace Traform SSB A
More informationSupervised Learning! B." Neural Network Learning! Typical Artificial Neuron! Feedforward Network! Typical Artificial Neuron! Equations!
Part 4B: Neura Networ earg 10/22/08 Superved earg B. Neura Networ earg Produce dered output for trag put Geeraze reaoaby appropratey to other put Good exampe: patter recogto Feedforward mutayer etwor 10/22/08
More informationCompetitive Facility Location Problem with Demands Depending on the Facilities
Aa Pacc Maageme Revew 4) 009) 5-5 Compeve Facl Locao Problem wh Demad Depedg o he Facle Shogo Shode a* Kuag-Yh Yeh b Hao-Chg Ha c a Facul of Bue Admrao Kobe Gau Uver Japa bc Urba Plag Deparme Naoal Cheg
More informationSpeech, NLP and the Web
peech NL ad he Web uhpak Bhaacharyya CE Dep. IIT Bombay Lecure 38: Uuperved learg HMM CFG; Baum Welch lecure 37 wa o cogve NL by Abh Mhra Baum Welch uhpak Bhaacharyya roblem HMM arg emac ar of peech Taggg
More informationA L A BA M A L A W R E V IE W
A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N
More informationChapter 5. Long Waves
ape 5. Lo Waes Wae e s o compaed ae dep: < < L π Fom ea ae eo o s s ; amos ozoa moo z p s ; dosac pesse Dep-aeaed coseao o mass
More informationLinear Regression Linear Regression with Shrinkage
Lear Regresso Lear Regresso h Shrkage Iroduco Regresso meas predcg a couous (usuall scalar oupu from a vecor of couous pus (feaures x. Example: Predcg vehcle fuel effcec (mpg from 8 arbues: Lear Regresso
More informationLaplace Transform. Definition of Laplace Transform: f(t) that satisfies The Laplace transform of f(t) is defined as.
Lplce Trfor The Lplce Trfor oe of he hecl ool for olvg ordry ler dfferel equo. - The hoogeeou equo d he prculr Iegrl re olved oe opero. - The Lplce rfor cover he ODE o lgerc eq. σ j ple do. I he pole o
More informationPractice Final Exam (corrected formulas, 12/10 11AM)
Ecoomc Meze. Ch Fall Socal Scece 78 Uvery of Wco-Mado Pracce Fal Eam (correced formula, / AM) Awer all queo he (hree) bluebook provded. Make cera you wre your ame, your ude I umber, ad your TA ame o all
More information11/8/2002 CS 258 HW 2
/8/ CS 58 HW. G o a a qc of aa h < fo a I o goa o co a C cc c F ch ha F fo a I A If cc - c a co h aoa coo o ho o choo h o qc? I o g o -coa o o-coa? W ca choo h o qc o h a a h aa a. Tha f o o a h o h a:.
More informationContinuous Time Markov Chains
Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,
More informationMidterm Exam. Tuesday, September hour, 15 minutes
Ecoomcs of Growh, ECON560 Sa Fracsco Sae Uvers Mchael Bar Fall 203 Mderm Exam Tuesda, Sepember 24 hour, 5 mues Name: Isrucos. Ths s closed boo, closed oes exam. 2. No calculaors of a d are allowed. 3.
More informationEMA5001 Lecture 3 Steady State & Nonsteady State Diffusion - Fick s 2 nd Law & Solutions
EMA5 Lecue 3 Seady Sae & Noseady Sae ffuso - Fck s d Law & Soluos EMA 5 Physcal Popees of Maeals Zhe heg (6) 3 Noseady Sae ff Fck s d Law Seady-Sae ffuso Seady Sae Seady Sae = Equlbum? No! Smlay: Sae fuco
More informationELEC 6041 LECTURE NOTES WEEK 3 Dr. Amir G. Aghdam Concordia University
ecre Noe Prepared b r G. ghda EE 64 ETUE NTE WEE r. r G. ghda ocorda Uer eceraled orol e - Whe corol heor appled o a e ha co of geographcall eparaed copoe or a e cog of a large ber of p-op ao ofe dered
More informationP a g e 5 1 of R e p o r t P B 4 / 0 9
P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e
More informationCHAPTER 2 Quadratic diophantine equations with two unknowns
CHAPTER - QUADRATIC DIOPHANTINE EQUATIONS WITH TWO UNKNOWNS 3 CHAPTER Quadraic diophaie equaio wih wo ukow Thi chaper coi of hree ecio. I ecio (A), o rivial iegral oluio of he biar quadraic diophaie equaio
More informationFault Diagnosis with Continuous-Time Parity Equations
Fau Dago h ouou-tme Par Equao Aee Shum Deparme of Maageme Far Eaer Feera Uver Rua Irouco Th chaper evoe o he probem of fau ago FD roboc a ura em o he bae of par equao. The erm "par equao" coe reae o he
More informationAlgorithms and Data Structures 2011/12 Week 9 Solutions (Tues 15th - Fri 18th Nov)
Algorihm and Daa Srucure 2011/ Week Soluion (Tue 15h - Fri 18h No) 1. Queion: e are gien 11/16 / 15/20 8/13 0/ 1/ / 11/1 / / To queion: (a) Find a pair of ube X, Y V uch ha f(x, Y) = f(v X, Y). (b) Find
More informationDensity estimation III. Linear regression.
Lecure 6 Mlos Hauskrec mlos@cs.p.eu 539 Seo Square Des esmao III. Lear regresso. Daa: Des esmao D { D D.. D} D a vecor of arbue values Obecve: r o esmae e uerlg rue probabl srbuo over varables X px usg
More informationChapter3 Pattern Association & Associative Memory
Cher3 Per Aoco & Aocve Memor Aocg er hch re mlr, corr, cloe roxm l, cloe ucceo emorl Aocve recll evoe oced er recll er b r of evoe/recll h comlee/ o er To e of oco. For o er d heero-oco! : relg o dffere
More informationLeast Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters
Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo
More informationReliability Analysis. Basic Reliability Measures
elably /6/ elably Aaly Perae faul Œ elably decay Teporary faul Œ Ofe Seady ae characerzao Deg faul Œ elably growh durg eg & debuggg A pace hule Challeger Lauch, 986 Ocober 6, Bac elably Meaure elably:
More information8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall
8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model
More informationLinear models for classification
CS 75 Mache Lear Lecture 9 Lear modes for cassfcato Mos Hausrecht mos@cs.ptt.edu 539 Seott Square ata: { d d.. d} d Cassfcato represets a dscrete cass vaue Goa: ear f : X Y Bar cassfcato A speca case he
More informationSolution set Stat 471/Spring 06. Homework 2
oluo se a 47/prg 06 Homework a Whe he upper ragular elemes are suppressed due o smmer b Le Y Y Y Y A weep o he frs colum o oba: A ˆ b chagg he oao eg ad ec YY weep o he secod colum o oba: Aˆ YY weep o
More informationReview - Week 10. There are two types of errors one can make when performing significance tests:
Review - Week Read: Chaper -3 Review: There are wo ype of error oe ca make whe performig igificace e: Type I error The ull hypohei i rue, bu we miakely rejec i (Fale poiive) Type II error The ull hypohei
More informationThe Poisson Process Properties of the Poisson Process
Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad
More informationCHAPTER 10: LINEAR DISCRIMINATION
CHAPER : LINEAR DISCRIMINAION Dscrmnan-based Classfcaon 3 In classfcaon h K classes (C,C,, C k ) We defned dscrmnan funcon g j (), j=,,,k hen gven an es eample, e chose (predced) s class label as C f g
More information14. Poisson Processes
4. Posso Processes I Lecure 4 we roduced Posso arrvals as he lmg behavor of Bomal radom varables. Refer o Posso approxmao of Bomal radom varables. From he dscusso here see 4-6-4-8 Lecure 4 " arrvals occur
More information(1) Cov(, ) E[( E( ))( E( ))]
Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )
More informationUpper Bound For Matrix Operators On Some Sequence Spaces
Suama Uer Bou formar Oeraors Uer Bou For Mar Oeraors O Some Sequece Saces Suama Dearme of Mahemacs Gaah Maa Uersy Yogyaara 558 INDONESIA Emal: suama@ugmac masomo@yahoocom Isar D alam aer aa susa masalah
More informationFORCED VIBRATION of MDOF SYSTEMS
FORCED VIBRAION of DOF SSES he respose of a N DOF sysem s govered by he marx equao of moo: ] u C] u K] u 1 h al codos u u0 ad u u 0. hs marx equao of moo represes a sysem of N smulaeous equaos u ad s me
More informationGENESIS. God makes the world
GENESIS 1 Go me he or 1 I he be Go me he b heve he erh everyh hh p he y. 2 There oh o he e erh. Noh ve here, oh *o ve here. There oy e eep er over he erh. There o h. I very r. The f Spr of Go move over
More informationConquering kings their titles take ANTHEM FOR CONGREGATION AND CHOIR
Coquerg gs her es e NTHEM FOR CONGREGTION ND CHOIR I oucg hs hm-hem, whch m be cuded Servce eher s Hm or s hem, he Cogrego m be referred o he No. of he Hm whch he words pper, d ved o o sgg he 1 s, 4 h,
More informationSolution. The straightforward approach is surprisingly difficult because one has to be careful about the limits.
ose ad Varably Homewor # (8), aswers Q: Power spera of some smple oses A Posso ose A Posso ose () s a sequee of dela-fuo pulses, eah ourrg depedely, a some rae r (More formally, s a sum of pulses of wdh
More informationOn Probability Density Function of the Quotient of Generalized Order Statistics from the Weibull Distribution
ISSN 684-843 Joua of Sac Voue 5 8 pp. 7-5 O Pobaby Dey Fuco of he Quoe of Geeaed Ode Sac fo he Webu Dbuo Abac The pobaby dey fuco of Muhaad Aee X k Y k Z whee k X ad Y k ae h ad h geeaed ode ac fo Webu
More informationθ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions:
Paramerc coug process models Cosder coug processes: N,,..., ha cou he occurreces of a eve of eres for dvduals Iesy processes: Lelhood λ ( ;,,..., N { } λ < Log-lelhood: l( log L( Score fucos: U ( l( log
More informationON THE EXTENSION OF WEAK ARMENDARIZ RINGS RELATIVE TO A MONOID
wwweo/voue/vo9iue/ijas_9 9f ON THE EXTENSION OF WEAK AENDAIZ INGS ELATIVE TO A ONOID Eye A & Ayou Eoy Dee of e Nowe No Uvey Lzou 77 C Dee of e Uvey of Kou Ou Su E-: eye76@o; you975@yooo ABSTACT Fo oo we
More informationDensity estimation. Density estimations. CS 2750 Machine Learning. Lecture 5. Milos Hauskrecht 5329 Sennott Square
Lecure 5 esy esmao Mlos Hauskrec mlos@cs..edu 539 Seo Square esy esmaos ocs: esy esmao: Mamum lkelood ML Bayesa arameer esmaes M Beroull dsrbuo. Bomal dsrbuo Mulomal dsrbuo Normal dsrbuo Eoeal famly Noaramerc
More informationImproved Exponential Estimator for Population Variance Using Two Auxiliary Variables
Improvd Epoal Emaor for Populao Varac Ug Two Aular Varabl Rajh gh Dparm of ac,baara Hdu Uvr(U.P., Ida (rgha@ahoo.com Pakaj Chauha ad rmala awa chool of ac, DAVV, Idor (M.P., Ida Flor maradach Dparm of
More informationEE 6885 Statistical Pattern Recognition
EE 6885 Sascal Paer Recogo Fall 005 Prof. Shh-Fu Chag hp://.ee.columba.edu/~sfchag Lecure 8 (/8/05 8- Readg Feaure Dmeso Reduco PCA, ICA, LDA, Chaper 3.8, 0.3 ICA Tuoral: Fal Exam Aapo Hyväre ad Erkk Oja,
More information700 STATEMENT OF ECONOMIC
R RM EME EM ERE H E H E HE E HE Y ERK HE Y P PRE MM 8 PUB UME ER PE Pee e k. ek, ME ER ( ) R) e -. ffe, ge, u ge e ( ue ) -- - k, B, e e,, f be Yu P eu RE) / k U -. f fg f ue, be he. ( ue ) ge: P:. Ju
More informationFault Tolerant Computing. Fault Tolerant Computing CS 530 Reliability Analysis
Probably /4/6 CS 5 elably Aaly Yahwa K. Malaya Colorado Sae very Ocober 4, 6 elably Aaly: Oule elably eaure: elably, avalably, Tra. elably, T M MTTF ad (, MTBF Bac Cae Sgle u wh perae falure, falure rae
More informationFundamentals of Speech Recognition Suggested Project The Hidden Markov Model
. Projec Iroduco Fudameals of Speech Recogo Suggesed Projec The Hdde Markov Model For hs projec, s proposed ha you desg ad mpleme a hdde Markov model (HMM) ha opmally maches he behavor of a se of rag sequeces
More informationc- : r - C ' ',. A a \ V
HS PAGE DECLASSFED AW EO 2958 c C \ V A A a HS PAGE DECLASSFED AW EO 2958 HS PAGE DECLASSFED AW EO 2958 = N! [! D!! * J!! [ c 9 c 6 j C v C! ( «! Y y Y ^ L! J ( ) J! J ~ n + ~ L a Y C + J " J 7 = [ " S!
More informationChapter 1 - Free Vibration of Multi-Degree-of-Freedom Systems - I
CEE49b Chaper - Free Vbrao of M-Degree-of-Freedo Syses - I Free Udaped Vbrao The basc ype of respose of -degree-of-freedo syses s free daped vbrao Aaogos o sge degree of freedo syses he aayss of free vbrao
More informationONE APPROACH FOR THE OPTIMIZATION OF ESTIMATES CALCULATING ALGORITHMS A.A. Dokukin
Iero Jor "Iforo Theore & co" Vo 463 ONE PPROH FOR THE OPTIIZTION OF ETITE UTING GORITH Do rc: I h rce he ew roch for ozo of eo ccg gorh ggeed I c e ed for fdg he correc gorh of coexy he coex of gerc roch
More informationt = s D Overview of Tests Two-Sample t-test: Independent Samples Independent Samples t-test Difference between Means in a Two-sample Experiment
Overview of Te Two-Sample -Te: Idepede Sample Chaper 4 z-te Oe Sample -Te Relaed Sample -Te Idepede Sample -Te Compare oe ample o a populaio Compare wo ample Differece bewee Mea i a Two-ample Experime
More information1 n. w = How much information can the network store? 4. RECURRENT NETWORKS. One stored pattern, x (1) : Since the elements of the vector x (1) should
4. RECURRENT NETWORKS Ho much formao ca he eor sore? Ths chaper preses some ypes of recurre eural eors: Frs, eors h (srog egh cosras are reaed, eemplfed by Hopfeld eors, folloed by a se of recurre eors
More informationState The position of school d i e t i c i a n, a created position a t S t a t e,
P G E 0 E C O E G E E FRDY OCOBER 3 98 C P && + H P E H j ) ) C jj D b D x b G C E Ob 26 C Ob 6 R H E2 7 P b 2 b O j j j G C H b O P G b q b? G P P X EX E H 62 P b 79 P E R q P E x U C Ob ) E 04 D 02 P
More informationPubH 7440 Spring 2010 Midterm 2 April
ubh 7440 Sprg 00 Mderm Aprl roblem a: Because \hea^_ s a lear combao of ormal radom arables wll also be ormal. Thus he mea ad arace compleel characerze he dsrbuo. We also use ha he Z ad \hea^{-}_ are depede.
More informationChapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)
Aoucemes Reags o E-reserves Proec roosal ue oay Parameer Esmao Bomercs CSE 9-a Lecure 6 CSE9a Fall 6 CSE9a Fall 6 Paer Classfcao Chaer 3: Mamum-Lelhoo & Bayesa Parameer Esmao ar All maerals hese sles were
More informationImproved Exponential Estimator for Population Variance Using Two Auxiliary Variables
Rajh gh Dparm of ac,baara Hdu Uvr(U.P.), Ida Pakaj Chauha, rmala awa chool of ac, DAVV, Idor (M.P.), Ida Flor maradach Dparm of Mahmac, Uvr of w Mco, Gallup, UA Improvd Epoal Emaor for Populao Varac Ug
More informationQuantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state)
Pro. O. B. Wrgh, Auum Quaum Mechacs II Lecure Tme-depede perurbao heory Tme-depede perurbao heory (degeerae or o-degeerae sarg sae) Cosder a sgle parcle whch, s uperurbed codo wh Hamloa H, ca exs a superposo
More informationP a g e 3 6 of R e p o r t P B 4 / 0 9
P a g e 3 6 of R e p o r t P B 4 / 0 9 p r o t e c t h um a n h e a l t h a n d p r o p e r t y fr om t h e d a n g e rs i n h e r e n t i n m i n i n g o p e r a t i o n s s u c h a s a q u a r r y. J
More informationTHE LOWELL LEDGER, INDEPENDENT NOT NEUTRAL.
E OE EDGER DEEDE O EUR FO X O 2 E RUO OE G DY OVEER 0 90 O E E GE ER E ( - & q \ G 6 Y R OY F EEER F YOU q --- Y D OVER D Y? V F F E F O V F D EYR DE OED UDER EDOOR OUE RER (E EYEV G G R R R :; - 90 R
More informationThe algebraic immunity of a class of correlation immune H Boolean functions
Ieraoal Coferece o Advaced Elecroc Scece ad Techology (AEST 06) The algebrac mmuy of a class of correlao mmue H Boolea fucos a Jgla Huag ad Zhuo Wag School of Elecrcal Egeerg Norhwes Uversy for Naoales
More informationCS623: Introduction to Computing with Neural Nets (lecture-10) Pushpak Bhattacharyya Computer Science and Engineering Department IIT Bombay
CS6: Iroducio o Compuig ih Neural Nes lecure- Pushpak Bhaacharyya Compuer Sciece ad Egieerig Deparme IIT Bombay Tilig Algorihm repea A kid of divide ad coquer sraegy Give he classes i he daa, ru he percepro
More informationOH BOY! Story. N a r r a t iv e a n d o bj e c t s th ea t e r Fo r a l l a g e s, fr o m th e a ge of 9
OH BOY! O h Boy!, was or igin a lly cr eat ed in F r en ch an d was a m a jor s u cc ess on t h e Fr en ch st a ge f or young au di enc es. It h a s b een s een by ap pr ox i ma t ely 175,000 sp ect at
More informationPARAMETER OPTIMIZATION FOR ACTIVE SHAPE MODELS. Contact:
PARAMEER OPIMIZAION FOR ACIVE SHAPE MODELS Chu Che * Mg Zhao Sa Z.L Jaju Bu School of Compuer Scece ad echology, Zhejag Uvery, Hagzhou, Cha Mcroof Reearch Cha, Bejg Sgma Ceer, Bejg, Cha Coac: chec@zju.edu.c
More informationLeast squares and motion. Nuno Vasconcelos ECE Department, UCSD
Leas squares ad moo uo Vascocelos ECE Deparme UCSD Pla for oda oda we wll dscuss moo esmao hs s eresg wo was moo s ver useful as a cue for recogo segmeao compresso ec. s a grea eample of leas squares problem
More informationSimple Linear Regression Analysis
LINEAR REGREION ANALYSIS MODULE II Lecture - 5 Smple Lear Regreo Aaly Dr Shalabh Departmet of Mathematc Stattc Ida Ittute of Techology Kapur Jot cofdece rego for A jot cofdece rego for ca alo be foud Such
More informationDensity estimation III.
Lecure 4 esy esmao III. Mlos Hauskrec mlos@cs..edu 539 Seo Square Oule Oule: esy esmao: Mamum lkelood ML Bayesa arameer esmaes MP Beroull dsrbuo. Bomal dsrbuo Mulomal dsrbuo Normal dsrbuo Eoeal famly Eoeal
More informationReal-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF
EDA/DIT6 Real-Tme Sysems, Chalmers/GU, 0/0 ecure # Updaed February, 0 Real-Tme Sysems Specfcao Problem: Assume a sysem wh asks accordg o he fgure below The mg properes of he asks are gve he able Ivesgae
More informationComputational learning and discovery
Computatoa earg ad dscover CSI 873 / MAH 689 Istructor: I. Grva Wedesda 7:2-1 pm Gve a set of trag data 1 1 )... ) { 1 1} fd a fucto that ca estmate { 1 1} gve ew ad mmze the frequec of the future error.
More informationSuppose we have observed values t 1, t 2, t n of a random variable T.
Sppose we have obseved vales, 2, of a adom vaable T. The dsbo of T s ow o belog o a cea ype (e.g., expoeal, omal, ec.) b he veco θ ( θ, θ2, θp ) of ow paamees assocaed wh s ow (whee p s he mbe of ow paamees).
More informationA PATRA CONFERINŢĂ A HIDROENERGETICIENILOR DIN ROMÂNIA,
A PATRA ONFERINŢĂ A HIDROENERGETIIENILOR DIN ROMÂNIA, Do Pael MODELLING OF SEDIMENTATION PROESS IN LONGITUDINAL HORIZONTAL TANK MODELAREA PROESELOR DE SEPARARE A FAZELOR ÎN DEANTOARE LONGITUDINALE Da ROBESU,
More informationIntroduction to Hypothesis Testing
Noe for Seember, Iroducio o Hyohei Teig Scieific Mehod. Sae a reearch hyohei or oe a queio.. Gaher daa or evidece (obervaioal or eerimeal) o awer he queio. 3. Summarize daa ad e he hyohei. 4. Draw a cocluio.
More informationA Simple Representation of the Weighted Non-Central Chi-Square Distribution
SSN: 9-875 raoa Joura o ovav Rarch Scc grg a Tchoogy (A S 97: 7 Cr rgaao) Vo u 9 Sbr A S Rrao o h Wgh No-Cra Ch-Squar Drbuo Dr ay A hry Dr Sahar A brah Dr Ya Y Aba Proor D o Mahaca Sac u o Saca Su a Rarch
More informationQR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA
QR facorzao Ay x real marx ca be wre as AQR, where Q s orhogoal ad R s upper ragular. To oba Q ad R, we use he Householder rasformao as follows: Le P, P, P -, be marces such ha P P... PPA ( R s upper ragular.
More informationFractal diffusion retrospective problems
Iraoa ora o App Mahac croc a Copr Avac Tchoo a Scc ISSN: 47-8847-6799 wwwaccor/iamc Ora Rarch Papr Fraca o rropcv prob O Yaro Rcv 6 h Ocobr 3 Accp 4 h aar 4 Abrac: I h arc w h rropcv vr prob Th rropcv
More informationFault Tolerant Computing. Fault Tolerant Computing CS 530 Probabilistic methods: overview
Probably 1/19/ CS 53 Probablsc mehods: overvew Yashwa K. Malaya Colorado Sae Uversy 1 Probablsc Mehods: Overvew Cocree umbers presece of uceray Probably Dsjo eves Sascal depedece Radom varables ad dsrbuos
More informationMathematical Formulation
Mahemacal Formulao The purpose of a fe fferece equao s o appromae he paral ffereal equao (PE) whle maag he physcal meag. Eample PE: p c k FEs are usually formulae by Taylor Seres Epaso abou a po a eglecg
More informationBayesian Separation of Non-Stationary Mixtures of Dependent Gaussian Sources
Bayea earao of No-aoary Mure of Deede Gaua ource Dez Geçağa rca. uruoğu Ayşı rüzü Boğazç Uvery ecrca ad ecroc geerg DearmeBebek3434 Đabu Turkey ITI ogo Nazoae dee cerche va G. Moruzz 564 Pa Iay Abrac.
More informationKEY. Math 334 Midterm I Fall 2008 sections 001 and 003 Instructor: Scott Glasgow
1 KEY Mah 4 Miderm I Fall 8 secions 1 and Insrucor: Sco Glasgow Please do NOT wrie on his eam. No credi will be given for such work. Raher wrie in a blue book, or on our own paper, preferabl engineering
More informationPartial Molar Properties of solutions
Paral Molar Properes of soluos A soluo s a homogeeous mxure; ha s, a soluo s a oephase sysem wh more ha oe compoe. A homogeeous mxures of wo or more compoes he gas, lqud or sold phase The properes of a
More informationCyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles
Ope Joural of Dsree Mahemas 2017 7 200-217 hp://wwwsrporg/joural/ojdm ISSN Ole: 2161-7643 ISSN Pr: 2161-7635 Cylally Ierval Toal Colorgs of Cyles Mddle Graphs of Cyles Yogqag Zhao 1 Shju Su 2 1 Shool of
More informationThe Linear Regression Of Weighted Segments
The Lear Regresso Of Weghed Segmes George Dael Maeescu Absrac. We proposed a regresso model where he depede varable s made o up of pos bu segmes. Ths suao correspods o he markes hroughou he da are observed
More informationTHIS PAGE DECLASSIFIED IAW E
THS PAGE DECLASSFED AW E0 2958 BL K THS PAGE DECLASSFED AW E0 2958 THS PAGE DECLASSFED AW E0 2958 B L K THS PAGE DECLASSFED AW E0 2958 THS PAGE DECLASSFED AW EO 2958 THS PAGE DECLASSFED AW EO 2958 THS
More informationFinal Exam Applied Econometrics
Fal Eam Appled Ecoomercs. 0 Sppose we have he followg regresso resl: Depede Varable: SAT Sample: 437 Iclded observaos: 437 Whe heeroskedasc-cosse sadard errors & covarace Varable Coeffce Sd. Error -Sasc
More information4. THE DENSITY MATRIX
4. THE DENSTY MATRX The desy marx or desy operaor s a alerae represeao of he sae of a quaum sysem for whch we have prevously used he wavefuco. Alhough descrbg a quaum sysem wh he desy marx s equvale o
More informationMultiphase Flow Simulation Based on Unstructured Grid
200 Tuoral School o Flud Dyamcs: Topcs Turbulece Uversy of Marylad, May 24-28, 200 Oule Bacgroud Mulphase Flow Smulao Based o Usrucured Grd Bubble Pacg Mehod mehod Based o he Usrucured Grd Remar B CHEN,
More informationSUMMATION OF INFINITE SERIES REVISITED
SUMMATION OF INFINITE SERIES REVISITED I several aricles over he las decade o his web page we have show how o sum cerai iiie series icludig he geomeric series. We wa here o eed his discussio o he geeral
More informationLecture VI Regression
Lecure VI Regresson (Lnear Mehods for Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure VI: MLSC - Dr. Sehu Vjayakumar Lnear Regresson Model M
More informationThe ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3.
C. Trael me cures for mulple reflecors The ray pahs ad rael mes for mulple layers ca be compued usg ray-racg, as demosraed Lab. MATLAB scrp reflec_layers_.m performs smple ray racg. (m) ref(ms) ref(ms)
More informationMoment Generating Function
1 Mome Geeraig Fucio m h mome m m m E[ ] x f ( x) dx m h ceral mome m m m E[( ) ] ( ) ( x ) f ( x) dx Mome Geeraig Fucio For a real, M () E[ e ] e k x k e p ( x ) discree x k e f ( x) dx coiuous Example
More informationThe conditional density p(x s ) Bayes rule explained. Bayes rule for a classification problem INF
INF 4300 04 Mulvarae clafcao Ae Solberg ae@fuoo Baed o Chaper -6 Duda ad Har: Paer Clafcao Baye rule for a clafcao proble Suppoe we have J, =,J clae he cla label for a pel, ad he oberved feaure vecor We
More informationTopic 2: Distributions, hypothesis testing, and sample size determination
Topc : Drbuo, hypohe eg, ad ample ze deermao. The Sude - drbuo [ST&D pp. 56, 77] Coder a repeaed drawg of ample of ze from a ormal drbuo. For each ample, compue,,, ad aoher ac,, where: ( ) The ac he devao
More informationBig O Notation for Time Complexity of Algorithms
BRONX COMMUNITY COLLEGE of he Ciy Uiversiy of New York DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE CSI 33 Secio E01 Hadou 1 Fall 2014 Sepember 3, 2014 Big O Noaio for Time Complexiy of Algorihms Time
More informationLecture 6: Learning for Control (Generalised Linear Regression)
Lecure 6: Learnng for Conrol (Generalsed Lnear Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure 6: RLSC - Prof. Sehu Vjayakumar Lnear Regresson
More informationCS 2750 Machine Learning Lecture 8. Linear regression. Supervised learning. a set of n examples
CS 75 Mache Learg Lecture 8 Lear regresso Mlos Hauskrecht los@cs.tt.eu 59 Seott Square Suervse learg Data: D { D D.. D} a set of eales D s a ut vector of sze s the esre outut gve b a teacher Obectve: lear
More informationREVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION
REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION I lear regreo, we coder the frequecy dtrbuto of oe varable (Y) at each of everal level of a ecod varable (X). Y kow a the depedet varable. The
More information( 1) β function for the Higgs quartic coupling λ in the standard model (SM) h h. h h. vertex correction ( h 1PI. Σ y. counter term Λ Λ.
funon for e Hs uar oun n e sanar moe (SM verex >< sef-ener ( PI Π ( - ouner erm ( m, ( Π m s fne Π s fne verex orreon ( PI Σ (,, ouner erm, ( reen funon ({ } Σ s fne Λ Λ Bn A n ( Caan-Smanz euaon n n (
More information