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fieguth p introduction pattern recognition machine learning 2022

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Textbook in PDF format The domains of Pattern Recognition and Machine Learning have experienced exceptional interest and growth, however the overwhelming number of methods and applications can make the fields seem bewildering. This text offers an accessible and conceptually rich introduction, a solid mathematical development emphasizing simplicity and intuition. Students beginning to explore pattern recognition do not need a suite of mathematically advanced methods or complicated computational libraries to understand and appreciate pattern recognition; rather the fundamental concepts and insights, eminently teachable at the undergraduate level, motivate this text. This book provides methods of analysis that the reader can realistically undertake on their own, supported by real-world examples, case-studies, and worked numerical / computational studies. List of Examples. List of Algorithms. Notation. Overview. Introduction to Pattern Recognition. Learning. Representing Patterns. Feature Extraction and Selection. Distance-Based Classiﬁcation. Inferring Class Models. Statistics-Based Classiﬁcation. Classiﬁer Testing and Validation. Discriminant-Based Classiﬁcation. Ensemble Classiﬁcation. Model-Free Classiﬁcation. Conclusions and Directions. Appendices

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Fieguth P. An Introduction to Pattern Recognition and Machine Learning 2022.pdf 20.1 MB

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fieguth p introduction pattern recognition machine learning 2022

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