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Maximum entropy is a powerful method for constructing statistical models of classification tasks, such as part of speech tagging in Natural Language Processing. Several example applications using maxent can be found in the OpenNLP Tools Library.
libit provides easy to use yet efficient tools for C for signal processing, coding, or scientific code in general. It includes basic vector, matrix and function types, some common source and channel coding tools such as quantization, entropy coding, etc.
...It is based on arithmetic coding compression. Just a proof of concept of the implementation of a statistical compressor. Aimple Java GUI interface provided. Best efficiency than huffman compressor. Entropy and information
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The Toolkit for Advanced Discriminative Modeling (TADM) is a C++ implementation for estimating the parameters of discriminative models, such as maximum entropy models. It uses the PETSc and TAO toolkits to provide high performance and scalability.
A code for fast multi-dimensional density estimation . Instead of assuming an a-priori metric definition, it calculates a locally adaptive metric for each data point by using, a Shannon Entropy based, binary space partitioning scheme.
Software to fit whole-sentence language models using the principle of maximum entropy. For developers of speech recognizers, text prediction interfaces, OCR, machine translation software.
...Basically this is related to the computation of the distribution of k co-occurrences of spatial events (generalising the contiguity distributions - 2 co-occurrences at distance 0) to derive spatial clustering statistics (mainly using the Shannon entropy, then called the k-spatial entropy) and methods linked to this: SOOk, SelSOOk (see caGEO paper) and scankOO (see TGIS). Another method (CAkOO) performing a k-Correspondence Analysis (i.e. on a multiway table with k entries) on the contingencies of co-occurrenceshas been already "published in my JSS paper about another R package: PTAk.
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