'Cross entropy' presentation slideshows

Relevance Models In Information Retrieval

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Pattern Recognition with N-Tuple Systems

Pattern Recognition with N-Tuple Systems. Simon Lucas Computer Science Dept Essex University. Overview. Standard Binary n-tuple Dealing with grey-levels Continuous n-tuple Bit-plane decomposition Dealing with sequences Scanning N-Tuple Future Directions. N-Tuple Systems.

504 views • 34 slides

Language Modeling

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Relevance-Based Language Models

Relevance-Based Language Models. Victor Lavrenko and W.Bruce Croft Department of Computer Science University of Massachusetts, Amherst, MA 01003 SIGIR 2001 Presented by Yi-Ting. Outline. Introduction

450 views • 20 slides

Learning to Rank: A Machine Learning Approach to Static Ranking

Learning to Rank: A Machine Learning Approach to Static Ranking. 049011 - Algorithms for Large Data Sets Student Symposium. Presented by Li-Tal Mashiach. Speaker: Li-Tal Mashiach. References . Learning to Rank Using Gradient Descent ICML, 2005, Burges et al

577 views • 19 slides

ABSTRACT

CROSS ENTROPY INFORMATION METRIC FOR QUANTIFICATION AND CLUSTER ANALYSIS OF ACCENTS Alireza Ghorshi Brunel University, London. ABSTRACT. An accent metric is introduced based on the cross entropy (CE) of the probability models (HMMs) of speech from different accents.

193 views • 19 slides

Cross-Entropy Based Testing

Cross-Entropy Based Testing. Hana Chockler, Benny Godlin, Eitan Farchi, Sergey Novikov. Research. Haifa, Israel. The problem: How to test for rare problems in large programs?. Testing involves running the program many times, hoping to find the problem.

195 views • 19 slides

Statistical NLP: Lecture 5

Statistical NLP: Lecture 5. Mathematical Foundations II: Information Theory. Entropy. The entropy is the average uncertainty of a single random variable. Let p(x)=P(X=x); where x ? X . H(p)= H(X)= - ? x? X p(x)log 2 p(x)

217 views • 9 slides

Statistical NLP: Lecture 5

Statistical NLP: Lecture 5. Mathematical Foundations II: Information Theory. Entropy. The entropy is the average uncertainty of a single random variable. Let p(x)=P(X=x); where x ? X . H(p)= H(X)= - ? x? X p(x)log 2 p(x)

93 views • 9 slides

Statistical NLP: Lecture 5

Statistical NLP: Lecture 5. Mathematical Foundations II: Information Theory (Ch 2). Entropy. The entropy is the average uncertainty of a single random variable. Let p(x)=P(X=x); where x ?X. H(p)= H(X)= - ? x ?X p(x)log 2 p(x) In other words, entropy measures the

96 views • 9 slides