for entropy
H = entropy(S)
this command will evaluate the entropy of S, S should be row matrix
H = entropy([X Y Z])
this command will find the joint entropy for the 3 variables
H = entropy([X,Y],[Z,W])
this will find H(X,Y/Z,W).. you can use it for any combination of joint entropies
Please validate this function before using it
Rainbow is a C program that performs document classification usingone of several different methods, including naive Bayes, TFIDF/Rocchio,K-nearest neighbor, Maximum entropy, Support Vector Machines, Fuhr sProbabilitistic Indexing, and a simple-minded form a shrinkage withnaive Bayes.
This file contains a new and improved version of the Huffman coder, (June 29. 2001). The name is Huff06.m. There are also some additional files which are helpful when using Matlab for data compression: quantizer, different variants of run-length-encoding and end-of-block coding in Mat2Vec, and a program which do JPEG-like entropy coding. A complete compression example is shown in TestMat2Vec.m. This file is all you need for Huffman coding in MatLab.
The Joint Video Team (JVT) of ISO/IEC MPEG and ITU-T VCEG are finalising a new standard for
the coding (compression) of natural video images. The new standard [1] will be known as H.264 and
also MPEG-4 Part 10, “Advanced Video Coding”. The standard specifies two types of entropy coding:
Context-based Adaptive Binary Arithmetic Coding (CABAC) and Variable-Length Coding (VLC).
This document provides a short introduction to CABAC. Familiarity with the concept of Arithmetic
Coding is assumed.
The Joint Video Team (JVT) of ISO/IEC MPEG and ITU-T VCEG are finalising a new standard for
the coding (compression) of natural video images. The new standard [1] will be known as H.264 and
also MPEG-4 Part 10, “Advanced Video Coding”. The standard specifies two types of entropy coding:
Context-based Adaptive Binary Arithmetic Coding (CABAC) and Variable-Length Coding (VLC).
The Variable-Length Coding scheme, part of the Baseline Profile of H.264, is described in this
document.
A dissipative particle swarm optimization is
developed according to the self-organization of dissipative
structure. The negative entropy is introduced to construct an
opening dissipative system that is far-from-equilibrium so as to
driving the irreversible evolution process with better fitness.
The testing of two multimodal functions indicates it improves
the performance effectively.
structure. The negative entropy is introduced to construct an
opening dissipative system that is far-from-equilibrium so as to
driving the irreversible evolution process with better fitness.
The testing of two multimodal functions indicates it improves
the performance effectively.
