Vassilis Katsouros, Vassilis Papavassiliou and Christos Emmanouilidis
|
|
|
- Blaise Snow
- 5 years ago
- Views:
Transcription
1 Vassilis Katsouros, Vassilis Papavassiliou ad Christos Emmaouilidis ATHENA Research & Iovatio Cetre, Greece christosem AT ieee.org
2 Problem defiitio Modelig Results Discussio - coclusio
3 Applied research, prototypes ad applicatios developmet Dissemiatio ad R&D results valorizatio Research Iovatio support Traiig ad kowledge trasfer 3
4 Idustrial Systems Istitute Istitute for the Maagemet of Iformatio Systems Istitute for Laguage ad Speech Processig Idustrial Iformatio & Commuicatio Systems Embedded Systems Eterprise Itegratio Security & Privacy Idustrial Automatio & Cotrol ICT i Maufacturig Large-scale iformatio systems Data maagemet Big data Geo-iformatics Databases Software egieerig Digital curatio Bio-iformatics E-Govermet Natural & Embodied Laguage Processig Multimedia, 2D/3D imagig & VR Itelliget Systems Speech ad Music Techology Techology-Ehaced Learig Cultural Iformatics Corallia Iovatio Clusters Develop iovative ecosystems i specific sectors ad regios of the coutry, ad where a competitive advatage ad export orietatio exists Nao/Microelectroics-based Systems ad Applicatios (mi-cluster) Iovative Gamig Techologies ad Creative Cotet (gi-cluster) Space Techologies ad Applicatios (si-cluster) Iitiatio of R&D Projects
5 welcom-project.ceti.gr Wireless sesor etworks for egieerig asset lifecycle optimal maagemet
6 Worked with differet modelig approaches Best results obtaied with a Bayesia classificatio approach defie distict problem classes oe for each problem type calculate the posterior probability of a test case for each problem class recommed the problem class that correspods to the maximum a posteriori (MAP) probability
7 case: cosists of a collectio of evet codes, each of which correspods to a umber of parameters a record of a case ca be defied as a sigle evet code alog with the respective measuremet parameters 30 parameters of oboard measuremets, recorded each time a evet code was geerated 1 dataset of cases with their evet codes ad respective parameters for traiig ad aother 1 for testig/evaluatio The traiig dataset icluded the classificatio of the cases ito uisace or problem for the cases classified as problems, the correspodig problem label/idetifier was also provided.
8 Traiig dataset: records that correspod to cases of which were characterized as uisace ad 164 as problem (13 distict problem IDs) Testig dataset: records of evet codes measured parameters that correspod to distict cases. Groud truth of the testig dataset ivolved 174 problem cases, with the remaiig beig uisace cases A recommeder should idetify the problem cases from the testig cases ad for each oe of them provide the respective problem idetifier. Evaluatio metric: calculated o a set of cases that ivolved all 174 groud truth problem cases ad a radom selectio of 174 from the total of uisace cases.
9 maximum performace: 348, i.e. sum of 174 groud truth problem cases ad 174 uisace that the traiig dataset icludes oe case with a problem type idetifier (P7940) that is ot foud i the test dataset test dataset icludes cases that have bee categorized i two problem types (P0932 ad P6880) for which there is o available traiig data Problem ID Traiig dataset Test dataset Number of Cases P P P P P P P P P P P P P P P Total
10 Pr P j C k Pr C k P j Pr[ C k Pr[ P ] j ] Bayes rule
11 P 1 C k P 2 P P C 3 P arg max log Pr Pr C P Pr log P PrP P M k jj j1... MMM j k k k jj j j
12 C C C C P Pr E E... E P Pr k j 1 2 k k k Nk j C C C E P Pr E P...Pr E P Pr j 1 2 k k k j Nk j Assumig idepedece amog evets
13 Cosider the case IDs as colums of a matrix NK N N K K K e e e e e e e e e e e e Cosider the evet IDs as rows of a matrix The elemets e ij of the matrix is the umber of occurreces of evet i beig observed i case j
14 Agai case IDs are the colums of the matrix With the rows beig the problem IDs Ad elemets 0 or 1
![C k P 1 C log Pr[ Ck Pj ] log](/docs-images/94/118958938/images/15-1.jpg "Pr E Pj P i1 2 P 3 P M N k k")
15 C k P 1 C log Pr[ Ck Pj ] log Pr E Pj P i1 2 P 3 P M N k k i
16 Cosider the problem IDs as colums of a matrix NM N N M M M Cosider the evet IDs as rows of a matrix The elemets ij of the matrix is the umber of occurreces of evet i is observed i a case that is classified i problem j
17 Add a ε term whe ij = 0 to avoid zero probabilities E 1 E 2 P 1 P 2 P M p p p p N1 p p p p E PrE 3 i Pj E N N 2 N p p p i1 p 1M 2M ij 3M NM ij
18 P 1 P 2 P M p p... 1 Pr P j 2 N i1 M N j1 i1 pij M ij
19 Problem ID Number of Cases Top-1 Top-3 Top-5 P P P P P P P P P P P P P Total Overall (%)
20 Problem ID Number of Cases Top-1 Top-2 Top- 3 Top-4 Top-5 P P P P P P P P P P P P Total Overall (%)
21 Gaussia Mixture Models (GMMs) for each problem ID traied o the case to evets parameters Diagoal covariace type No. of Gaussia mixtures 10 Best score: 18 Support Vector Machies (SVM) classifiers with features the case to evets parameters Radial Basis Kerel Best score: 35
22 A hybrid techique comprisig: (a) A recommeder for problem type ID (b) A classifier for uisace / problem Tried the proposed (a) approach with a SVM-based (b) approach but the gai i uisace rate detectio was balaced by the loss i problem ID idetificatio ad there was o time left for further improvemets
23 A Bayesia Approach for the Maiteace Actio Recommedatio 2013 PHM Data Challege was proposed A hybrid techique could achieve improved results The defiitio of evaluatio metrics for differet PHM problems is a key issue How about data challeges with multiple objectives?
24 Vassilis Katsouros, Vassilis Papavassiliou ad Christos Emmaouilidis ATHENA Research & Iovatio Cetre, Greece christosem AT ieee.org
Information-based Feature Selection
Iformatio-based Feature Selectio Farza Faria, Abbas Kazeroui, Afshi Babveyh Email: {faria,abbask,afshib}@staford.edu 1 Itroductio Feature selectio is a topic of great iterest i applicatios dealig with
Chapter 7. Support Vector Machine
Chapter 7 Support Vector Machie able of Cotet Margi ad support vectors SVM formulatio Slack variables ad hige loss SVM for multiple class SVM ith Kerels Relevace Vector Machie Support Vector Machie (SVM)
Enterprise interoperability measurement - Basic concepts
Eterprise iteroperability measuremet - Basic cocepts Nicolas Dacli, David Che, Bruo Vallespir LAPS/GRAI, Uiversity Bordeaux 1, ENSEIRB, UMR 5131 CNRS, 351 cours de la Libératio, 33405 Talece Cedex, Frace
ECE 8527: Introduction to Machine Learning and Pattern Recognition Midterm # 1. Vaishali Amin Fall, 2015
ECE 8527: Itroductio to Machie Learig ad Patter Recogitio Midterm # 1 Vaishali Ami Fall, 2015 tue39624@temple.edu Problem No. 1: Cosider a two-class discrete distributio problem: ω 1 :{[0,0], [2,0], [2,2],
15-780: Graduate Artificial Intelligence. Density estimation
5-780: Graduate Artificial Itelligece Desity estimatio Coditioal Probability Tables (CPT) But where do we get them? P(B)=.05 B P(E)=. E P(A B,E) )=.95 P(A B, E) =.85 P(A B,E) )=.5 P(A B, E) =.05 A P(J
Vector Quantization: a Limiting Case of EM
. Itroductio & defiitios Assume that you are give a data set X = { x j }, j { 2,,, }, of d -dimesioal vectors. The vector quatizatio (VQ) problem requires that we fid a set of prototype vectors Z = { z
Title: Damage Identification of Structures Based on Pattern Classification Using Limited Number of Sensors
Cover page Title: Damage Idetificatio of Structures Based o Patter Classificatio Usig Limited Number of Sesors Authors: Yuyi QIAN Akira MITA PAPER DEADLINE: **JULY, ** PAPER LENGTH: **8 PAGES MAXIMUM **
CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering
CEE 5 Autum 005 Ucertaity Cocepts for Geotechical Egieerig Basic Termiology Set A set is a collectio of (mutually exclusive) objects or evets. The sample space is the (collectively exhaustive) collectio
Statistical Pattern Recognition
Statistical Patter Recogitio Classificatio: No-Parametric Modelig Hamid R. Rabiee Jafar Muhammadi Sprig 2014 http://ce.sharif.edu/courses/92-93/2/ce725-2/ Ageda Parametric Modelig No-Parametric Modelig
Research on Dependable level in Network Computing System Yongxia Li 1, a, Guangxia Xu 2,b and Shuangyan Liu 3,c
Applied Mechaics ad Materials Olie: 04-0-06 ISSN: 66-748, Vols. 53-57, pp 05-08 doi:0.408/www.scietific.et/amm.53-57.05 04 Tras Tech Publicatios, Switzerlad Research o Depedable level i Network Computig
1 Review of Probability & Statistics
1 Review of Probability & Statistics a. I a group of 000 people, it has bee reported that there are: 61 smokers 670 over 5 960 people who imbibe (drik alcohol) 86 smokers who imbibe 90 imbibers over 5
Hybridized Heredity In Support Vector Machine
Hybridized Heredity I Suort Vector Machie May 2015 Hybridized Heredity I Suort Vector Machie Timothy Idowu Yougmi Park Uiversity of Wiscosi-Madiso idowu@stat.wisc.edu yougmi@stat.wisc.edu May 2015 Abstract
6.867 Machine learning
6.867 Machie learig Mid-term exam October, ( poits) Your ame ad MIT ID: Problem We are iterested here i a particular -dimesioal liear regressio problem. The dataset correspodig to this problem has examples
Stochastic Matrices in a Finite Field
Stochastic Matrices i a Fiite Field Abstract: I this project we will explore the properties of stochastic matrices i both the real ad the fiite fields. We first explore what properties 2 2 stochastic matrices
CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 5
CS434a/54a: Patter Recogitio Prof. Olga Veksler Lecture 5 Today Itroductio to parameter estimatio Two methods for parameter estimatio Maimum Likelihood Estimatio Bayesia Estimatio Itroducto Bayesia Decisio
Discrete Mathematics and Probability Theory Spring 2013 Anant Sahai Lecture 18
EECS 70 Discrete Mathematics ad Probability Theory Sprig 2013 Aat Sahai Lecture 18 Iferece Oe of the major uses of probability is to provide a systematic framework to perform iferece uder ucertaity. A
Mixtures of Gaussians and the EM Algorithm
Mixtures of Gaussias ad the EM Algorithm CSE 6363 Machie Learig Vassilis Athitsos Computer Sciece ad Egieerig Departmet Uiversity of Texas at Arligto 1 Gaussias A popular way to estimate probability desity
Expectation-Maximization Algorithm.
Expectatio-Maximizatio Algorithm. Petr Pošík Czech Techical Uiversity i Prague Faculty of Electrical Egieerig Dept. of Cyberetics MLE 2 Likelihood.........................................................................................................
Continuous Domain Analysis of Graph Laplacian Regularization for Image Denoisng. Presenter: Gene Cheung National Institute of Informatics
Cotiuous Domai Aalysis of Graph Laplacia Regularizatio for Image Deoisg Preseter: Gee Cheug atioal Istitute of Iformatics [1] Jiahao Pag, Gee Cheug, Wei Hu, Oscar C. Au, "Redefiig Self-Similarity i atural
Short Term Load Forecasting Using Artificial Neural Network And Imperialist Competitive Algorithm
Short Term Load Forecastig Usig Artificial eural etwork Ad Imperialist Competitive Algorithm Mostafa Salamat, Mostafa_salamat63@yahoo.com Javad Mousavi, jmousavi.sh1365@gmail.com Seyed Hamid Shah Alami,
Clustering. CM226: Machine Learning for Bioinformatics. Fall Sriram Sankararaman Acknowledgments: Fei Sha, Ameet Talwalkar.
Clusterig CM226: Machie Learig for Bioiformatics. Fall 216 Sriram Sakararama Ackowledgmets: Fei Sha, Ameet Talwalkar Clusterig 1 / 42 Admiistratio HW 1 due o Moday. Email/post o CCLE if you have questios.
Outline. CSCI-567: Machine Learning (Spring 2019) Outline. Prof. Victor Adamchik. Mar. 26, 2019
Outlie CSCI-567: Machie Learig Sprig 209 Gaussia mixture models Prof. Victor Adamchik 2 Desity estimatio U of Souther Califoria Mar. 26, 209 3 Naive Bayes Revisited March 26, 209 / 57 March 26, 209 2 /
As stated by Laplace, Probability is common sense reduced to calculation.
Note: Hadouts DO NOT replace the book. I most cases, they oly provide a guidelie o topics ad a ituitive feel. The math details will be covered i class, so it is importat to atted class ad also you MUST
Probability and MLE.
10-701 Probability ad MLE http://www.cs.cmu.edu/~pradeepr/701 (brief) itro to probability Basic otatios Radom variable - referrig to a elemet / evet whose status is ukow: A = it will rai tomorrow Domai
Generalized Semi- Markov Processes (GSMP)
Geeralized Semi- Markov Processes (GSMP) Summary Some Defiitios Markov ad Semi-Markov Processes The Poisso Process Properties of the Poisso Process Iterarrival times Memoryless property ad the residual
Uncertainty. Variables. assigns to each sentence numerical degree of belief between 0 and 1. uncertainty
Bayes Classificatio Ucertaity & robability Baye's rule Choosig Hypotheses- Maximum a posteriori Maximum Likelihood - Baye's cocept learig Maximum Likelihood of real valued fuctio Bayes optimal Classifier
Linear Algebra Issues in Wireless Communications
Rome-Moscow school of Matrix Methods ad Applied Liear Algebra August 0 September 18, 016 Liear Algebra Issues i Wireless Commuicatios Russia Research Ceter [vladimir.lyashev@huawei.com] About me ead of
Supplementary Material: HCP: A Flexible CNN Framework for Multi-label Image Classification
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL.XX, NO.XX, 2015 1 Supplemetary Material: HCP: A Flexible CNN Framework for Multi-label Image Classificatio Yuchao Wei, Wei Xia, Mi Li,
DERIVING THE 12-LEAD ECG FROM EASI ELECTRODES VIA NONLINEAR REGRESSION
DERIVING THE 1-LEAD ECG FROM EASI ELECTRODES VIA NONLINEAR REGRESSION 1 PIROON KAEWFOONGRUNGSI, DARANEE HORMDEE 1, Departmet of Computer Egieerig, Faculty of Egieerig, Kho Kae Uiversity 4, Thailad E-mail:
Pattern Classification
Patter Classificatio All materials i these slides were tae from Patter Classificatio (d ed) by R. O. Duda, P. E. Hart ad D. G. Stor, Joh Wiley & Sos, 000 with the permissio of the authors ad the publisher
μ are complex parameters. Other
A New Numerical Itegrator for the Solutio of Iitial Value Problems i Ordiary Differetial Equatios. J. Suday * ad M.R. Odekule Departmet of Mathematical Scieces, Adamawa State Uiversity, Mubi, Nigeria.
Structuring Element Representation of an Image and Its Applications
Iteratioal Joural of Cotrol Structurig Automatio Elemet ad Represetatio Systems vol. of a o. Image 4 pp. ad 50955 Its Applicatios December 004 509 Structurig Elemet Represetatio of a Image ad Its Applicatios
Machine Learning.
10-701 Machie Learig http://www.cs.cmu.edu/~epxig/class/10701-15f/ Orgaizatioal ifo All up-to-date ifo is o the course web page (follow liks from my page). Istructors - Eric Xig - Ziv Bar-Joseph TAs: See
Accuracy assessment methods and challenges
Accuracy assessmet methods ad challeges Giles M. Foody School of Geography Uiversity of Nottigham giles.foody@ottigham.ac.uk Backgroud Need for accuracy assessmet established. Cosiderable progress ow see
Outline. Linear regression. Regularization functions. Polynomial curve fitting. Stochastic gradient descent for regression. MLE for regression
REGRESSION 1 Outlie Liear regressio Regularizatio fuctios Polyomial curve fittig Stochastic gradiet descet for regressio MLE for regressio Step-wise forward regressio Regressio methods Statistical techiques
IP Reference guide for integer programming formulations.
IP Referece guide for iteger programmig formulatios. by James B. Orli for 15.053 ad 15.058 This documet is iteded as a compact (or relatively compact) guide to the formulatio of iteger programs. For more
Statistical Analysis on Uncertainty for Autocorrelated Measurements and its Applications to Key Comparisons
Statistical Aalysis o Ucertaity for Autocorrelated Measuremets ad its Applicatios to Key Comparisos Nie Fa Zhag Natioal Istitute of Stadards ad Techology Gaithersburg, MD 0899, USA Outlies. Itroductio.
Lectures on Stochastic System Analysis and Bayesian Updating
Lectures o Stochastic System Aalysis ad Bayesia Updatig Jue 29-July 13 2005 James L. Beck, Califoria Istitute of Techology Jiaye Chig, Natioal Taiwa Uiversity of Sciece & Techology Siu-Kui (Iva) Au, Nayag
Forecasting SO 2 air pollution in Salamanca, Mexico using an ADALINE.
Iovative Productio Machies ad Systems D.T. Pham, E.E. Eldukhri ad A.J. Soroka (eds) 2008 MEC. Cardiff Uiversity, UK. Forecastig SO 2 air pollutio i Salamaca, Mexico usig a ADALINE. M.G. Cortia a, U.S.
1 Review and Overview
DRAFT a fial versio will be posted shortly CS229T/STATS231: Statistical Learig Theory Lecturer: Tegyu Ma Lecture #3 Scribe: Migda Qiao October 1, 2013 1 Review ad Overview I the first half of this course,
Presenting A Framework To Study Linkages Among Tqm Practices, Supply Chain Management Practices, And Performance Using Dematel Technique
Australia Joural of Basic ad Applied Scieces, 5(9): 885-890, 20 ISSN 99-878 Presetig A Framework To Study Likages Amog Tqm Practices, Supply Chai Maagemet Practices, Ad Performace Usig Dematel Techique
Generating Functions. 1 Operations on generating functions
Geeratig Fuctios The geeratig fuctio for a sequece a 0, a,..., a,... is defied to be the power series fx a x. 0 We say that a 0, a,... is the sequece geerated by fx ad a is the coefficiet of x. Example
3/8/2016. Contents in latter part PATTERN RECOGNITION AND MACHINE LEARNING. Dynamical Systems. Dynamical Systems. Linear Dynamical Systems
Cotets i latter part PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 13: SEQUENTIAL DATA Liear Dyamical Systems What is differet from HMM? Kalma filter Its stregth ad limitatio Particle Filter Its simple
Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7:
0 Multivariate Cotrol Chart 3 Multivariate Normal Distributio 5 Estimatio of the Mea ad Covariace Matrix 6 Hotellig s Cotrol Chart 6 Hotellig s Square 8 Average Value of k Subgroups 0 Example 3 3 Value
INF Introduction to classifiction Anne Solberg Based on Chapter 2 ( ) in Duda and Hart: Pattern Classification
INF 4300 90 Itroductio to classifictio Ae Solberg ae@ifiuioo Based o Chapter -6 i Duda ad Hart: atter Classificatio 90 INF 4300 Madator proect Mai task: classificatio You must implemet a classificatio
Chapter 3: Other Issues in Multiple regression (Part 1)
Chapter 3: Other Issues i Multiple regressio (Part 1) 1 Model (variable) selectio The difficulty with model selectio: for p predictors, there are 2 p differet cadidate models. Whe we have may predictors
Quality prediction for polypropylene production process based on. CLGPR model
uality predictio for polypropylee productio process based o CLGPR model Zhiiag Ge 1, ao Che, Zhihua Sog 1 1 State Key Laboratory of Idustrial Cotrol echology, Istitute of Idustrial Process Cotrol, Zhejiag
Research Article A Unified Weight Formula for Calculating the Sample Variance from Weighted Successive Differences
Discrete Dyamics i Nature ad Society Article ID 210761 4 pages http://dxdoiorg/101155/2014/210761 Research Article A Uified Weight Formula for Calculatig the Sample Variace from Weighted Successive Differeces
Channel coding, linear block codes, Hamming and cyclic codes Lecture - 8
Digital Commuicatio Chael codig, liear block codes, Hammig ad cyclic codes Lecture - 8 Ir. Muhamad Asial, MSc., PhD Ceter for Iformatio ad Commuicatio Egieerig Research (CICER) Electrical Egieerig Departmet
EDGE AND SECANT IDEALS OF SHARED-VERTEX GRAPHS
EDGE AND SECANT IDEALS OF SHARED-VERTEX GRAPHS ZVI ROSEN Abstract. We examie miimal free resolutios ad Betti diagrams of the edge ad secat ideals of oe family of graphs. We demostrate how splittig the
Lecture 10: Performance Evaluation of ML Methods
CSE57A Machie Learig Sprig 208 Lecture 0: Performace Evaluatio of ML Methods Istructor: Mario Neuma Readig: fcml: 5.4 (Performace); esl: 7.0 (Cross-Validatio); optioal book: Evaluatio Learig Algorithms
LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity
LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 9 Multicolliearity Dr Shalabh Departmet of Mathematics ad Statistics Idia Istitute of Techology Kapur Multicolliearity diagostics A importat questio that
A Unified Model between the OWA Operator and the Weighted Average in Decision Making with Dempster-Shafer Theory
Proceedigs of the World Cogress o Egieerig 200 Vol I, Jue 30 - July 2, 200, Lodo, U.K. A Uified Model betwee the OWA Operator ad the Weighted Average i Decisio Makig with Dempster-Shafer Theory José M.
Intermittent demand forecasting by using Neural Network with simulated data
Proceedigs of the 011 Iteratioal Coferece o Idustrial Egieerig ad Operatios Maagemet Kuala Lumpur, Malaysia, Jauary 4, 011 Itermittet demad forecastig by usig Neural Network with simulated data Nguye Khoa
Improvement of Generic Attacks on the Rank Syndrome Decoding Problem
Improvemet of Geeric Attacks o the Rak Sydrome Decodig Problem Nicolas Arago, Philippe Gaborit, Adrie Hauteville, Jea-Pierre Tillich To cite this versio: Nicolas Arago, Philippe Gaborit, Adrie Hauteville,
ECE 901 Lecture 12: Complexity Regularization and the Squared Loss
ECE 90 Lecture : Complexity Regularizatio ad the Squared Loss R. Nowak 5/7/009 I the previous lectures we made use of the Cheroff/Hoeffdig bouds for our aalysis of classifier errors. Hoeffdig s iequality
Discrete Mathematics and Probability Theory Fall 2009 Satish Rao,David Tse Lecture 16. Multiple Random Variables and Applications to Inference
CS 70 Discrete Mathematics ad Probability Theory Fall 2009 Satish Rao,David Tse Lecture 16 Multiple Radom Variables ad Applicatios to Iferece I may probability problems, we have to deal with multiple r.v.
FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures
FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING Lectures MODULE 5 STATISTICS II. Mea ad stadard error of sample data. Biomial distributio. Normal distributio 4. Samplig 5. Cofidece itervals
Machine Learning Theory Tübingen University, WS 2016/2017 Lecture 12
Machie Learig Theory Tübige Uiversity, WS 06/07 Lecture Tolstikhi Ilya Abstract I this lecture we derive risk bouds for kerel methods. We will start by showig that Soft Margi kerel SVM correspods to miimizig
The target reliability and design working life
Safety ad Security Egieerig IV 161 The target reliability ad desig workig life M. Holický Kloker Istitute, CTU i Prague, Czech Republic Abstract Desig workig life ad target reliability levels recommeded
7. Modern Techniques. Data Encryption Standard (DES)
7. Moder Techiques. Data Ecryptio Stadard (DES) The objective of this chapter is to illustrate the priciples of moder covetioal ecryptio. For this purpose, we focus o the most widely used covetioal ecryptio
Estimation of Population Mean Using Co-Efficient of Variation and Median of an Auxiliary Variable
Iteratioal Joural of Probability ad Statistics 01, 1(4: 111-118 DOI: 10.593/j.ijps.010104.04 Estimatio of Populatio Mea Usig Co-Efficiet of Variatio ad Media of a Auxiliary Variable J. Subramai *, G. Kumarapadiya
A Unified Approach on Fast Training of Feedforward and Recurrent Networks Using EM Algorithm
2270 IEEE TRASACTIOS O SIGAL PROCESSIG, VOL. 46, O. 8, AUGUST 1998 [12] Q. T. Zhag, K. M. Wog, P. C. Yip, ad J. P. Reilly, Statistical aalysis of the performace of iformatio criteria i the detectio of
EE 6885 Statistical Pattern Recognition
EE 6885 Statistical Patter Recogitio Fall 5 Prof. Shih-Fu Chag http://www.ee.columbia.edu/~sfchag Lecture 6 (9/8/5 EE6887-Chag 6- Readig EM for Missig Features Textboo, DHS 3.9 Bayesia Parameter Estimatio
Warped, Chirp Z-Transform: Radar Signal Processing
arped, Chirp Z-Trasform: Radar Sigal Processig by Garimella Ramamurthy Report o: IIIT/TR// Cetre for Commuicatios Iteratioal Istitute of Iformatio Techology Hyderabad - 5 3, IDIA Jauary ARPED, CHIRP Z
Extreme Value Charts and Analysis of Means (ANOM) Based on the Log Logistic Distribution
Joural of Moder Applied Statistical Methods Volume 11 Issue Article 0 11-1-01 Extreme Value Charts ad Aalysis of Meas (ANOM) Based o the Log Logistic istributio B. Sriivasa Rao R.V.R & J.C. College of
1 Efficient Splice Site Prediction with Context-Sensitive Distance Kernels
1 Efficiet Splice Site Predictio with Cotext-Sesitive Distace Kerels Berard Maderick, Feg Liu ad Bram Vaschoewikel This paper presets a compariso betwee differet cotext-sesitive kerel fuctios for doig
Solution of Final Exam : / Machine Learning
Solutio of Fial Exam : 10-701/15-781 Machie Learig Fall 2004 Dec. 12th 2004 Your Adrew ID i capital letters: Your full ame: There are 9 questios. Some of them are easy ad some are more difficult. So, if
The Pascal Fibonacci numbers
Notes o Number Theory ad Discrete Mathematics Vol. 19, 013, No. 3, 5 11 The Pascal Fiboacci umbers J. V. Leyedekkers 1 ad A. G. Shao 1 Faculty of Sciece, The Uiversity of Sydey NSW 00, Australia Faculty
The AMSU Observation Bias Correction and Its Application Retrieval Scheme, and Typhoon Analysis
The AMSU Observatio Bias Correctio ad Its Applicatio Retrieval Scheme, ad Typhoo Aalysis Chie-Be Chou, Kug-Hwa Wag Cetral Weather Bureau, Taipei, Taiwa, R.O.C. Abstract Sice most of AMSU chaels have a
Evapotranspiration Estimation Using Support Vector Machines and Hargreaves-Samani Equation for St. Johns, FL, USA
Evirometal Egieerig 0th Iteratioal Coferece eissn 2029-7092 / eisbn 978-609-476-044-0 Vilius Gedimias Techical Uiversity Lithuaia, 27 28 April 207 Article ID: eviro.207.094 http://eviro.vgtu.lt DOI: https://doi.org/0.3846/eviro.207.094
Linear Classifiers III
Uiversität Potsdam Istitut für Iformatik Lehrstuhl Maschielles Lere Liear Classifiers III Blaie Nelso, Tobias Scheffer Cotets Classificatio Problem Bayesia Classifier Decisio Liear Classifiers, MAP Models
Sequential Monte Carlo Methods - A Review. Arnaud Doucet. Engineering Department, Cambridge University, UK
Sequetial Mote Carlo Methods - A Review Araud Doucet Egieerig Departmet, Cambridge Uiversity, UK http://www-sigproc.eg.cam.ac.uk/ ad2/araud doucet.html ad2@eg.cam.ac.uk Istitut Heri Poicaré - Paris - 2
RAINFALL PREDICTION BY WAVELET DECOMPOSITION
RAIFALL PREDICTIO BY WAVELET DECOMPOSITIO A. W. JAYAWARDEA Departmet of Civil Egieerig, The Uiversit of Hog Kog, Hog Kog, Chia P. C. XU Academ of Mathematics ad Sstem Scieces, Chiese Academ of Scieces,
The prediction of cement energy demand based on support vector machine Xin Zhang a, *, Shaohong Jing b
4th Iteratioal Coferece o Computer, Mechatroics, Cotrol ad Electroic Egieerig (ICCMCEE 05) The predictio of cemet eergy demad based o support vector machie Xi Zhag a,, Shaohog Jig b School of Electrical
2 1. The r.s., of size n2, from population 2 will be. 2 and 2. 2) The two populations are independent. This implies that all of the n1 n2
Chapter 8 Comparig Two Treatmets Iferece about Two Populatio Meas We wat to compare the meas of two populatios to see whether they differ. There are two situatios to cosider, as show i the followig examples:
Empirical Process Theory and Oracle Inequalities
Stat 928: Statistical Learig Theory Lecture: 10 Empirical Process Theory ad Oracle Iequalities Istructor: Sham Kakade 1 Risk vs Risk See Lecture 0 for a discussio o termiology. 2 The Uio Boud / Boferoi
FLOWSHOP SCHEDULING USING A NETWORK APPROACH
,*, A. M. H. Oladeide 1,*, A. I. Momodu 2 ad C. A. Oladeide 3 1, 2, 3DEPARTMENT OF PRODUCTION ENGINEERING, FACULTY OF ENGINEERING, UNIVERSITY OF BENIN, NIGERIA. E-mail addresses: 1 moladeide@uibe.edu,
Bi-Magic labeling of Interval valued Fuzzy Graph
Advaces i Fuzzy Mathematics. ISSN 0973-533X Volume 1, Number 3 (017), pp. 645-656 Research Idia Publicatios http://www.ripublicatio.com Bi-Magic labelig of Iterval valued Fuzzy Graph K.Ameeal Bibi 1 ad
Tracking Bearing Degradation using Gaussian Wavelets
Trackig Bearig Degradatio usig Gaussia Wavelets Toy Galati 1 1 Aerospace Divisio, Defece Sciece ad Techology Group, 56 Lorimer Street, Fishermas Bed, Victoria, 37, Australia Abstract This paper ivestigates
Optimally Sparse SVMs
A. Proof of Lemma 3. We here prove a lower boud o the umber of support vectors to achieve geeralizatio bouds of the form which we cosider. Importatly, this result holds ot oly for liear classifiers, but
INTEGRATION BY PARTS (TABLE METHOD)
INTEGRATION BY PARTS (TABLE METHOD) Suppose you wat to evaluate cos d usig itegratio by parts. Usig the u dv otatio, we get So, u dv d cos du d v si cos d si si d or si si d We see that it is ecessary
Bayesian Methods: Introduction to Multi-parameter Models
Bayesia Methods: Itroductio to Multi-parameter Models Parameter: θ = ( θ, θ) Give Likelihood p(y θ) ad prior p(θ ), the posterior p proportioal to p(y θ) x p(θ ) Margial posterior ( θ, θ y) is Iterested
ANY TIME PROBABILISTIC REASONING FOR SENSOR VALIDATION
266 ANY TIME PROBABILISTIC REASONING FOR SENSOR VALIDATION P.H. Ibargiiegoytia Istituto de Ivestigacioes Eh ctricas, A.P. 1-475 Cueravaca, Mor., 62001, Mexico pibar@iie.org.mx L.E. Sucar Istituto Tecol6gico
TEACHER CERTIFICATION STUDY GUIDE
COMPETENCY 1. ALGEBRA SKILL 1.1 1.1a. ALGEBRAIC STRUCTURES Kow why the real ad complex umbers are each a field, ad that particular rigs are ot fields (e.g., itegers, polyomial rigs, matrix rigs) Algebra
AUTOMATED ANALYSIS OF EDDY CURRENT (EC) GIANT MAGNETO RESISTIVE (GMR) DATA
AUTOATED AAYSIS OF EDDY CURRET (EC) GIAT AGETO RESISTIVE (GR) DATA Y. Deg, X. iu, Z. Zeg, B. Koltebah, R. Bossi, G. Steffes 3, ad. Udpa DE aboratory, Departmet of Electrical ad Computer Egieerig, ichiga
Axioms of Measure Theory
MATH 532 Axioms of Measure Theory Dr. Neal, WKU I. The Space Throughout the course, we shall let X deote a geeric o-empty set. I geeral, we shall ot assume that ay algebraic structure exists o X so that
Parallel Vector Algorithms David A. Padua
Parallel Vector Algorithms 1 of 32 Itroductio Next, we study several algorithms where parallelism ca be easily expressed i terms of array operatios. We will use Fortra 90 to represet these algorithms.
Provläsningsexemplar / Preview TECHNICAL REPORT INTERNATIONAL SPECIAL COMMITTEE ON RADIO INTERFERENCE
TECHNICAL REPORT CISPR 16-4-3 2004 AMENDMENT 1 2006-10 INTERNATIONAL SPECIAL COMMITTEE ON RADIO INTERFERENCE Amedmet 1 Specificatio for radio disturbace ad immuity measurig apparatus ad methods Part 4-3:
HARMONIC ANALYSIS FOR OPTICALLY MODULATING BODIES USING THE HARMONIC STRUCTURE FUNCTION (HSF) Lockheed Martin Hawaii
HARMONIC ANALYSIS FOR OPTICALLY MODULATING BODIES USING THE HARMONIC STRUCTURE FUNCTION (HSF) Dr. R. David Dikema Chief Scietist Mr. Scot Seto Chief Egieer Lockheed Marti Hawaii Abstract Lockheed Marti
Pattern Classification
Patter Classificatio All materials i these slides were tae from Patter Classificatio (d ed) by R. O. Duda, P. E. Hart ad D. G. Stor, Joh Wiley & Sos, 000 with the permissio of the authors ad the publisher
An Introduction to Randomized Algorithms
A Itroductio to Radomized Algorithms The focus of this lecture is to study a radomized algorithm for quick sort, aalyze it usig probabilistic recurrece relatios, ad also provide more geeral tools for aalysis
62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +
62. Power series Defiitio 16. (Power series) Give a sequece {c }, the series c x = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + is called a power series i the variable x. The umbers c are called the coefficiets of
Deep Neural Networks CMSC 422 MARINE CARPUAT. Deep learning slides credit: Vlad Morariu
Deep Neural Networks CMSC 422 MARINE CARPUAT marie@cs.umd.edu Deep learig slides credit: Vlad Morariu Traiig (Deep) Neural Networks Computatioal graphs Improvemets to gradiet descet Stochastic gradiet
Kinetics of Complex Reactions
Kietics of Complex Reactios by Flick Colema Departmet of Chemistry Wellesley College Wellesley MA 28 wcolema@wellesley.edu Copyright Flick Colema 996. All rights reserved. You are welcome to use this documet
PARETO-OPTIMAL SOLUTION OF A SCHEDULING PROBLEM ON A SINGLE MACHINE WITH PERIODIC MAINTENANCE AND NON-PRE-EMPTIVE JOBS
Proceedigs of the Iteratioal Coferece o Mechaical Egieerig 2007 (ICME2007) 2-3 December 2007, Dhaka, Bagladesh ICME07-AM-6 PARETO-OPTIMAL SOLUTION OF A SCHEDULING PROBLEM ON A SINGLE MACHINE WITH PERIODIC
CHAPTER 5 SOME MINIMAX AND SADDLE POINT THEOREMS
CHAPTR 5 SOM MINIMA AND SADDL POINT THORMS 5. INTRODUCTION Fied poit theorems provide importat tools i game theory which are used to prove the equilibrium ad eistece theorems. For istace, the fied poit
GUIDELINES ON REPRESENTATIVE SAMPLING
DRUGS WORKING GROUP VALIDATION OF THE GUIDELINES ON REPRESENTATIVE SAMPLING DOCUMENT TYPE : REF. CODE: ISSUE NO: ISSUE DATE: VALIDATION REPORT DWG-SGL-001 002 08 DECEMBER 2012 Ref code: DWG-SGL-001 Issue
A Note on Effi cient Conditional Simulation of Gaussian Distributions. April 2010
A Note o Effi ciet Coditioal Simulatio of Gaussia Distributios A D D C S S, U B C, V, BC, C April 2010 A Cosider a multivariate Gaussia radom vector which ca be partitioed ito observed ad uobserved compoetswe
Hypothesis Testing. Evaluation of Performance of Learned h. Issues. Trade-off Between Bias and Variance
Hypothesis Testig Empirically evaluatig accuracy of hypotheses: importat activity i ML. Three questios: Give observed accuracy over a sample set, how well does this estimate apply over additioal samples?