Pattern Recognition: Classification, Feature Selection, Template Matching, Clustering, Dimensionality Reduction, Artificial Neural Networks.
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Feature and Dimensionality Reduction for Clustering with Deep Learning (Unsupervised and Semi-Supervised Learning)
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Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability, Series Number 20)
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Pattern Recognition
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Pattern Recognition and Machine Learning (Information Science and Statistics)
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Introduction to Pattern Recognition: A Matlab Approach
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Fundamentals of Pattern Recognition and Machine Learning
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Fuzzy C-means Clustering using Pattern Recognition: Concepts, Methods, Implementations
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Fundamentals of Pattern Recognition and Machine Learning
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Ensemble Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
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Some of those treating more speciрїв¬рѓc parts (such as clustering) are cited in the appropriate chapter of this book.
Pattern recognition tasks mentioned above.
There are many other books on pattern recognition.
Solving problems in pattern recognition and image processing.
This software allows one to perform discriminant analysis, clustering and color distribution analysis.