'Conditional entropy' diaporamas de présentation

Information Bottleneck

Information Bottleneck. presented by Boris Epshtein & Lena Gorelick. Advanced Topics in Computer and Human Vision Spring 2004. Agenda. Motivation Information Theory - Basic Definitions Rate Distortion Theory Blahut-Arimoto algorithm Information Bottleneck Principle IB algorithms iIB

1.67k views • 114 slides

Assessing complexity of short-term heart period variability through entropy-based approaches

Assessing complexity of short-term heart period variability through entropy-based approaches. Alberto Porta. Department of Biomedical Sciences for Health Galeazzi Orthopedic Institute University of Milan Milan, Italy. Introduction.

777 views • 66 slides

Information and Coding Theory Transmission over lossless channels. Entropy. Compression codes -

Information and Coding Theory Transmission over lossless channels. Entropy. Compression codes - Shannon code, Huffman code, arithmetic code. Juris Viksna, 2014. Information transmission. We will focus on compression/decompression parts, assuming that there are no losses during transmission.

965 views • 59 slides

Decision Trees on MapReduce

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A New Method of Probability Density Estimation for Mutual Information Based Image Registration

A New Method of Probability Density Estimation for Mutual Information Based Image Registration. Ajit Rajwade, Arunava Banerjee, Anand Rangarajan. Dept. of Computer and Information Sciences & Engineering, University of Florida. Image Registration: problem definition.

499 views • 38 slides

Information-Theoretic Measures for Anomaly Detection

Information-Theoretic Measures for Anomaly Detection. Wenke Lee, and Dong Xiang (North Carolina State University). IEEE Security and Privacy, 2001. Speaker: Chang Huan Wu 2009/4/14. Outline. Introduction Information-Theoretic Measures Case Studies Conclusions. Introduction (1/2).

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459 views • 30 slides

Information Gain, Decision Trees and Boosting

Information Gain, Decision Trees and Boosting. 10-701 ML recitation 9 Feb 2006 by Jure. Entropy and Information Grain. Entropy & Bits. You are watching a set of independent random sample of X X has 4 possible values: P(X=A)=1/4, P(X=B)=1/4, P(X=C)=1/4, P(X=D)=1/4

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Office hours shifted: 2-4 Tuesdays Summary & Discussion of Gell-Mann Definitions & Examples

Office hours shifted: 2-4 Tuesdays Summary & Discussion of Gell-Mann Definitions & Examples Shannon Information Mutual Information Kolmogorov Complexity (AIC) Turing Machine Effective Complexity. Shannon Information. Shannon Entropy H to measure basic information capacity:

392 views • 26 slides

An intuitive introduction to information theory

An intuitive introduction to information theory. Ivo Grosse Leibniz Institute of Plant Genetics and Crop Plant Research Gatersleben Bioinformatics Centre Gatersleben-Halle. Outline. Why information theory? An intuitive introduction. History of biology. St. Thomas Monastry, Brno. Genetics.

371 views • 26 slides

ITEC 810 Entropy based anomaly detection systems

ITEC 810 Entropy based anomaly detection systems. Name:Xin Heng Student No:41738799 Supervisor: Professor Vijay Varadharajan. Contents:. 1.Background 2.Introduction 3. Information theory 4. UNM sendmail system call data case study 5. CID compare to traditional matrix 6. Conclusion.

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SNLP Chapter 2 Mathematical Foundation

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2. Mathematical Foundations

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Conditional Computational Entropy

Conditional Computational Entropy. Chun-Yuan Hsiao ( Boston University, USA) Joint work with Chi-Jen Lu ( Academia Sinica, Taiwan) Leonid Reyzin ( Boston University, USA). Does Pseudo-Entropy = Incompressibility?. How to extract more pseudorandom bits?. Shannon Entropy. ? ?

296 views • 18 slides

Maximum Entropy

Maximum Entropy.

244 views • 16 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

Opinionated

215 views • 8 slides

Entropy Rate of a Markov Chain

Entropy Rate of a Markov Chain. For a stationary Markov chain the entropy rate is given by: Where the conditional entropy is computed using the given stationary distribution. Recall that the stationary distribution ? is the solution of the equations:

526 views • 7 slides