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Kernel Methods and Machine Learning - S. Y. Kung

Kernel Methods and Machine Learning

By: S. Y. Kung

eText | 17 April 2014 | Edition Number 1

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Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.

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Non-FictionComputing & I.T.Computer ScienceArtificial IntelligenceMachine LearningComputer VisionMedicineNursing & Ancillary ServicesBiomedical EngineeringEngineering & TechnologyEnergy Technology & EngineeringElectrical Engineering
Digital Signal Processing (DSP)

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