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Author: Admin | 2025-04-28
And also overfitting are discussed. Chapter 6 presentsa classical architecture, referred to as radial-basis function networks. Even though this architecture is no longer used frequently, it is important because it represents a direct generalization of the kernel support-vector machine. Advanced architectures and applications: A lot of thesuccess in neural network design is a result of the specializedarchitectures for various domains and applications. Examples ofsuch specialized architectures include graph neural networks, recurrent neural networks andconvolutional neural networks. Since the specialized architecturesform the key to the understanding of neural network performance invarious domains, most of the book will be devoted to this setting.Several advanced topics like deep reinforcement learning, neural Turing mechanisms, and generativeadversarial networks are discussed. Some of the ``forgotten'' architectures like RBF networks andKohonen self-organizing maps are included because of their potential in many applications.The book is written for graduate students, researchers, andpractitioners. The book does require knowledge of probability andlinear algebra. Furthermore basic knowledge of machine learning ishelpful. Numerous exercises are available along with a solutionmanual to aid in classroom teaching. Where possible, anapplication-centric view is highlighted in order to give the readera feel for the technology. MACHINE LEARNING FOR TEXT Machine Learning for Text (Springer), Authored by Charu Aggarwal, April 2018. -- Comprehensive textbook on machine learning for Text. Table of Contents Book page PDF Download Link (Free for computers connected to subscribing institutions only) Buy hard-cover or PDF (for general public- The PDF has embedded links and can be loaded on a kindle reader. The PDF version's equations read better on a kindle e-reader than the kindle edition from Amazon) This book covers machine learning techniques fromtext using both bag-of-words and sequence-centric methods. The scope ofcoverage is vast, and it includes traditional information retrieval methodsand also recent methodsfrom neural networks and deep learning. Thechapters of this book can be organized into three categories: Classical machine learning methods: These chapters discuss theclassical machine learning methods such as matrix factorization, topic modeling, dimensionality reduction,clustering, classification, linear models, and evaluation. All these techniques treat text as a bag of words. Contextual learning methods that combinedifferent types of text and also combine text with heterogeneous data types are covered. Classical information retrieval and search engines: Although this book is focused on text mining, the importance of retrieval and ranking methodsin mining applications is quite significant. Therefore, the book covers the key aspects ofinformation retrieval, such as data structures, Web ranking, crawling,
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