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Estimation of distribution algorithm based on hidden Markov models for combinatorial optimization
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Stochastic variational inference
The Journal of Machine Learning Research
Word classification for sentiment polarity estimation using neural network
HCI International'13 Proceedings of the 15th international conference on Human Interface and the Management of Information: information and interaction design - Volume Part I
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Topic segmentation and labeling in asynchronous conversations
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Perturbative corrections for approximate inference in Gaussian latent variable models
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Quizz: targeted crowdsourcing with a billion (potential) users
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Computational Intelligence and Neuroscience - Special issue on Modeling and Analysis of Neural Spike Trains
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Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.