Code For Pattern Recognition And Machine Learning

pattern_recognition_tool

a tool for marking samples in images for database building, also including algorithm of LBP,HOG,and classifiers of SVM (six kernels), adaboost,BP and convolutional networks, extreme learning machine.

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Deep Semantic Ranking Based Hashing

This algorithm is described in Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval. For more details, please visit https://github.com/zhaofang0627/cuda-convnet-for-hashing

AuthorsFang Zhao
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Auto-encoder Based Data Clustering Toolkit

The auto-encoder based data clustering toolkit provides a quick start of clustering based on deep auto-encoder nets. This toolkit can cluster data in feature space with a deep nonlinear nets.

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Uncorrelated Multilinear Discriminant Analysis

This archive contains a Matlab implementation of the Uncorrelated Multilinear Discriminant Analysis (UMLDA) algorithm (as well as its regularized and aggregated versions), as described in the paper: Haiping Lu, K.N. Plataniotis, and A.N. Venetsanopoulo ...

AuthorsHaiping Lu
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Uncorrelated Multilinear Principal Component Analysis

This archive contains a Matlab implementation of the Uncorrelated Multilinear Principal Component Analysis (UMPCA) algorithm, as described in the paper: Haiping Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "Uncorrelated Multilinear Principal Compone ...

AuthorsHaiping Lu
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Multilinear Principal Component Analysis

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This archive contains a Matlab implementation of the Multilinear Principal Component Analysis (MPCA) algorithm and MPCA+LDA, as described in the paper Haiping Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "MPCA: Multilinear Principal Component Analys ...

AuthorsHaiping LU
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Naive Bayes EM Algorithm

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OpenPR-NBEM is an C++ implementation of Naive Bayes Classifier, which is a well-known generative classification algorithm for the application such as text classification. The Naive Bayes algorithm requires the probabilistic distribution to be discrete. Op ...

AuthorsRui XIA
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Two-stage Sparse Representation

This program implements a novel robust sparse representation method, called the two-stage sparse representation (TSR), for robust recognition on a large-scale database. Based on the divide and conquer strategy, TSR divides the procedure of robust recogni ...

AuthorsRan HE
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CMatrix Class

It's a C++ program for symmetric matrix diagonalization, inversion and principal component anlaysis(PCA). The matrix diagonalization function can also be applied to the computation of singular value decomposition (SVD), Fisher linear discriminant analysis ...

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Linear Discriminant Function Classifier

This program is a C++ implementation of Linear Discriminant Function Classifier. Discriminant functions such as perceptron criterion, cross entropy (CE) criterion, and least mean square (LMS) criterion (all for multi-class classification problems) are sup ...

AuthorsRui Xia
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Naive Bayes Classifier

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This program is a C++ implementation of Naive Bayes Classifier, which is a well-known generative classification algorithm for the application such as text classification. The Naive Bayes algorithm requires the probabilistic distribution to be discrete. Th ...

AuthorsRui Xia
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Supervised Latent Semantic Indexing

Supervised Latent Semantic Indexing(SLSI) is an supervised feature transformation method. The algorithms in this package are based on the iterative algorithm of Latent Semantic Indexing.

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Layer-Based Dependency Parser

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LDPar is an efficient data-driven dependency parser. You can train your own parsing model on treebank data and parse new data using the induced model.

AuthorsPing Jian
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Probabilistic Latent Semantic Indexing

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Calculate Normalized Information Measures

The toolbox is to calculate normalized information measures from a given m by (m+1) confusion matrix for objective evaluations of an abstaining classifier. It includes total 24 normalized information measures based on three groups of definitions, that is, ...

AuthorsBaogang Hu
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Agglomerative Mean-Shift Clustering

Mean-Shift (MS) is a powerful non-parametric clustering method. Although good accuracy can be achieved, its computational cost is particularly expensive even on moderate data sets. For the purpose of algorithm speedup, an agglomerative MS clustering metho ...

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