Pattern Recognition and Machine Learning (Information Science and Statistics)
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Probability Theory: The Logic of Science
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Digital Image Processing (3rd Edition)
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Algorithmic Game Theory
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)
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Elements of Information Theory 2nd Edition (Wiley Series in Telecommunications and Signal Processing)
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
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Semi-Supervised Learning (Adaptive Computation and Machine Learning)
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Numerical Optimization (Springer Series in Operations Research and Financial Engineering)
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The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
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Handbook of Mathematical Models in Computer Vision
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Understanding Complex Datasets: Data Mining with Matrix Decompositions (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
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Hands-on Morphological Image Processing (SPIE Tutorial Texts in Optical Engineering Vol. TT59)
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Introduction to Computational Genomics: A Case Studies Approach
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Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
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Numerical Geometry of Images: Theory, Algorithms, and Applications
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Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
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Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
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Topology for Computing (Cambridge Monographs on Applied and Computational Mathematics)
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Statistics and Analysis of Shapes (Modeling and Simulation in Science, Engineering and Technology)
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Elements of Pattern Theory (Johns Hopkins Studies in the Mathematical Sciences)
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Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence)
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