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The top documents tagged [training set d]

ppt
ppt
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Microsoft PowerPoint - ml4textweb00
Microsoft PowerPoint - ml4textweb00
1.326 views
boosting algorithm
boosting algorithm
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Transductive Reliability Estimation for Kernel Based Classifiers 1 Department of Computer Science, University of Ioannina, Greece 2 Faculty of Computer
Transductive Reliability Estimation for Kernel Based Classifiers 1 Department of Computer Science, University of Ioannina, Greece 2 Faculty of Computer
220 views
Perceptual and Sensory Augmented Computing Machine Learning, WS 13/14 Machine Learning – Lecture 11 AdaBoost 02.12.2013 Bastian Leibe RWTH Aachen
Perceptual and Sensory Augmented Computing Machine Learning, WS 13/14 Machine Learning – Lecture 11 AdaBoost 02.12.2013 Bastian Leibe RWTH Aachen
222 views
Learning In Bayesian Networks. General Learning Problem Set of random variables X = {X 1, X 2, X 3, X 4, …} Training set D = { X (1), X (2), …, X (N)
Learning In Bayesian Networks. General Learning Problem Set of random variables X = {X 1, X 2, X 3, X 4, …} Training set D = { X (1), X (2), …, X (N)
218 views
Contextual Classification with Functional Max-Margin Markov Networks Dan MunozDrew Bagnell Nicolas VandapelMartial Hebert
Contextual Classification with Functional Max-Margin Markov Networks Dan MunozDrew Bagnell Nicolas VandapelMartial Hebert
224 views
Introduction to Machine Learning Algorithms. 2 What is Artificial Intelligence (AI)? Design and study of computer programs that behave intelligently
Introduction to Machine Learning Algorithms. 2 What is Artificial Intelligence (AI)? Design and study of computer programs that behave intelligently
219 views
On Appropriate Assumptions to Mine Data Streams: Analyses and Solutions Jing Gao† Wei Fan‡ Jiawei Han† †University of Illinois at Urbana-Champaign ‡IBM
On Appropriate Assumptions to Mine Data Streams: Analyses and Solutions Jing Gao† Wei Fan‡ Jiawei Han† †University of Illinois at Urbana-Champaign ‡IBM
216 views
Learning In Bayesian Networks. Learning Problem Set of random variables X = {W, X, Y, Z, …} Training set D = { x 1, x 2, …, x N } Each observation specifies
Learning In Bayesian Networks. Learning Problem Set of random variables X = {W, X, Y, Z, …} Training set D = { x 1, x 2, …, x N } Each observation specifies
218 views
Soft Margin Estimation for Speech Recognition Main Reference: Jinyu Li, " SOFT MARGIN ESTIMATION FOR AUTOMATIC SPEECH RECOGNITION," PhD thesis, Georgia
Soft Margin Estimation for Speech Recognition Main Reference: Jinyu Li, " SOFT MARGIN ESTIMATION FOR AUTOMATIC SPEECH RECOGNITION," PhD thesis, Georgia
222 views
Graphical Models in Machine Learning AI4190. 2 Outlines of Tutorial 1. Machine Learning and Bioinformatics Machine Learning Problems in Bioinformatics
Graphical Models in Machine Learning AI4190. 2 Outlines of Tutorial 1. Machine Learning and Bioinformatics Machine Learning Problems in Bioinformatics
231 views