Machine Learning
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Machine Learning: An Artificial Intelligence Approach
By Ryszard S. Michalski, Jaime G. Carbonell, Tom M. MitchellGoldstein, I. P. and Grissom, E., “Annotated Production Systems: a Model for Skill Acquisition”, Fifth International Joint Conference on Artificial Intelligence, Cambridge, Mass., pp. 311-317, 1977. 195. Goldstein, M. M. ...
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Machine Learning: A Probabilistic Perspective
By Kevin P. MurphyIn UIAL Meila , M. and T. Jaakkola ( 2006 ) . Mitchell , T. ( 1997 ) . Machine Learning . Tractable Bayesian learning of tree Minka , T. ( 1999 ) . Pathologies of ortho McGraw Hill . belief networks . Statistics and Com dox statisies .
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Machine Learning: Modeling Data Locally and Globally
By Michael R. Lyu, Kai-Zhu Huang, Haiqin YangSpecifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning"or "global learning.
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Machine Learning: The Art and Science of Algorithms that Make Sense of Data
By Peter Flach(1992). Stacked generalization. Neural Networks 5(2):241—259. 342 Zadrozny, 380 References.
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Machine Learning: Hands-On for Developers and Technical Professionals
By Jason BellLearn more from your data with this hands-on guide to machine learning If you want to get into machine learning but fear the math, this book is your ultimate guide.
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Machine Learning: Proceedings of the Ninth International Workshop (ML92)
By Peter Edwards, D. SleemanMachine Learning Proceedings 1992.
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Machine Learning: Advanced Techniques and Emerging Applications
By Hamed FarhadiKernel-based data fusion, denoted as kernel fusion, has been pioneered by Lanckriet et al. [20] as a statistical learning framework for genomic data fusion and has been applied widely in various applications. In particular, kernel ...
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Machine Learning: Methods and Applications to Brain Disorders
By Andrea Mechelli, Sandra Viera... for using multimodal data, one is left wondering why there are not more multimodal machine learning studies being ... Kernel-based data integration methods are arguably some of the most prevalent strategies for combining multimodal ...
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Machine Learning: An Applied Mathematics Introduction
By Paul WilmottThis resource covers the essential mathematics behind all of the following topics: K Nearest Neighbours; K Means Clustering; Na-ve Bayes Classifier; Regression Methods; Support Vector Machines; Self-Organizing Maps; Decision Trees; Neural ...
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Machine Learning: Concepts, Methodologies, Tools and Applications
By Management Association, Information ResourcesIn Proceedings of the 5th International Working Conference on Intelligent VirtualAgents (IVA2005), Kos, Greece. Rana, A. S., & Zalzala, A. M. (1997). Fifth International Conference onArtificial Neural Networks (Conf. Publ. No. 440).
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Machine Learning: End-To-End Guide for Java Developers
By Bostjan Kaluza, Richard M. Reese, Jennifer L. ReeseDevelop, Implement and Tuneup your Machine Learning applications using the power of Java programmingAbout This Book* Detailed coverage on key machine learning topics with an emphasis on both theoretical and practical aspects* Address ...
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Machine Learning: Discriminative and Generative
By Tony JebaraAlso, this bridge brings forth new hybrid discriminative-generative tools that combine the strengths of both camps. This book serves multiple purposes as well.
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Machine Learning: A Bayesian and Optimization Perspective
By Sergios TheodoridisNew to this edition: Complete re-write of the chapter on Neural Networks and Deep Learning to reflect the latest advances since the 1st edition.
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Machine Learning: New and Collected Stories
By Hugh HoweyA collection of new and previously published stories by New York Times best-selling author Hugh Howey, including three set in his Wool universe
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Machine Learning: A Practical Approach on the Statistical Learning Theory
By RODRIGO F MELLO, Moacir Antonelli PontiThis book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes.
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Machine Learning: A Constraint-Based Approach
By Marco GoriThis book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, and includes many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which ...
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Machine Learning: An Artificial Intelligence Approach
By Ryszard Stanisław Michalski, Jaime Guillermo Carbonell, Tom Michael MitchellMachine Learning: An Artificial Intelligence Approach
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Machine Learning: The Art and Science of Algorithms that Make Sense of Data
By Peter FlachAs one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles.
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Machine Learning
By Zhi-Hua ZhouThe book can be used as an undergraduate or postgraduate textbook for computer science, computer engineering, electrical engineering, data science, and related majors.
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Machine Learning: Hands-On for Developers and Technical Professionals
By Jason BellGo deep data diving with this hands-on guide to machine learning If you want to get into machine learning but fear the math, this book is your ultimate guide.