jo taeho (31 Ergebnisse)

hardcover. Zustand: Very Good.

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Zustand: New.

Taschenbuch. Zustand: Neu. Neuware - This book is concerned with table-based machine learning algorithms which are applied to word classification. The book is authored by collecting slides about the application of machine learning algorithms to word classification; each page is given as a slide. This book is intended for ones who study applications of machine learning algorithms to word classification and is composed of five sections. Word classification is defined as the process of assigning a category or some categories to each word among the predefined ones in this book, the scope is restricted to only semantic word classification. This book covers the four machine learning algorithms, KNN algorithm, Naïve Bayes, Learning Vector Quantization, and Perceptron, as approaches to word classification, and they are modified into their table-based versions.

Zustand: New. pp. 340.

Taschenbuch. Zustand: Neu. Neuware - This book is concerned with machine learning algorithms which are applied to text classification. The book is authored by collecting slides about the application of string vector-based machine learning algorithms to text classification; each page is given as a slide. This book is intended for ones who study applications of machine learning algorithms to text classification and is composed of five sections. The scope of this book is defined as text classification which is defined as the process of assigning a category or some categories to each text, and machine learning algorithms which are modified into string vector-based ones. This book covers the four machine learning algorithms, string vector-based KNN Algorithm, string vector -based Naïve Bayes, string vector -based Learning Vector Quantization, and string vector -based Perceptron, as approaches to text classification.

Taschenbuch. Zustand: Neu. Neuware - This book is concerned with graph-based machine learning algorithms which are applied to word clustering. The book is authored by collecting slides about the application of machine learning algorithms to word clustering; each page is given as a slide. This book is intended for ones who study applications of machine learning algorithms to word clustering and is composed of five sections. Word clustering is defined as the process of segmenting a word group into subgroups each of which contains semantically similar words; in this book, the scope is restricted to only semantic word clustering. This book covers graph based modified versions of AHC Algorithm, K means algorithm, Linear Online Clustering Algorithm, and Divisive Algorithm, as approaches to word clustering.

Taschenbuch. Zustand: Neu. Neuware - This book is concerned with graph-based machine learning algorithms which are applied to text classification. The book is authored by collecting slides about the application of graph-based machine learning algorithms to text classification; each page is given as a slide. This book is intended for ones who study applications of machine learning algorithms to text classification and is composed of five sections. The scope of this book is defined as text classification which is defined as the process of assigning a category or some categories to each text, and machine learning algorithms which are modified into graph-based ones. This book covers the four machine learning algorithms, graph-based KNN Algorithm, graph -based Naïve Bayes, graph -based Learning Vector Quantization, and graph -based Perceptron, as approaches to text classification.

Taschenbuch. Zustand: Neu. Neuware - This book is concerned with numerical vector-based machine learning algorithms which are applied to keyword extraction. The book is authored by collecting slides about the application of machine learning algorithms to keyword extraction; each page is given as a slide. This book is intended for ones who study applications of machine learning algorithms to keyword extraction and is composed of five sections. Keyword extraction is defined as the process of extracting important words selectively from a text. This book covers the four machine learning algorithms, KNN Algorithm, Naïve Bayes, Learning Vector Quantization, and Perceptron, as approaches to keyword extraction.

Taschenbuch. Zustand: Neu. Neuware - This book is concerned with string vector-based machine learning algorithms which are applied to word clustering. The book is authored by collecting slides about the application of machine learning algorithms to word clustering; each page is given as a slide. This book is intended for ones who study applications of machine learning algorithms to word clustering and is composed of five sections. Word clustering is defined as the process of segmenting a word group into subgroups each of which contains semantically similar words; in this book, the scope is restricted to only semantic word clustering. This book covers string vector based modified versions of AHC Algorithm, K means algorithm, Linear Online Clustering Algorithm, and Divisive Algorithm, as approaches to word clustering.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher.

Hardcover. Zustand: Brand New. 2nd edition. 462 pages. 9.26x6.11x9.21 inches. In Stock.

Buch. Zustand: Neu. Neuware -This popular book, updated as a textbook for classroom use, discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management. The book features exercises and code to help readers quickly learn and apply knowledge.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 464 pp. Englisch.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This popular book, updated as a textbook for classroom use, discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management. The book features exercises and code to help readers quickly learn and apply knowledge.

Taschenbuch. Zustand: Neu. Deep Learning Foundations | Taeho Jo | Taschenbuch | xx | Englisch | 2024 | Springer | EAN 9783031328817 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Neuware -This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book¿s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 448 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book's third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.

Zustand: New.

Taschenbuch. Zustand: Neu. Machine Learning Foundations | Supervised, Unsupervised, and Advanced Learning | Taeho Jo | Taschenbuch | xx | Englisch | 2022 | Springer Nature Switzerland | EAN 9783030659028 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Text Mining | Concepts, Implementation, and Big Data Challenge | Taeho Jo | Taschenbuch | xiii | Englisch | 2019 | Springer Nature Switzerland | EAN 9783030063023 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Neuware -This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning.Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning;Outlines the computation paradigm for solving classification, regression, and clustering;Features essential techniques for building the a new generation of machine learning.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 412 pp. Englisch.

Buch. Zustand: Neu. Neuware -This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning.Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning;Outlines the computation paradigm for solving classification, regression, and clustering;Features essential techniques for building the a new generation of machine learning.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 412 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning.Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning;Outlines the computation paradigm for solving classification, regression, and clustering;Features essential techniques for building the a new generation of machine learning.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning.Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning;Outlines the computation paradigm for solving classification, regression, and clustering;Features essential techniques for building the a new generation of machine learning.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher.

Buch. Zustand: Neu. Neuware -This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book¿s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 448 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book's third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.

Hardcover. Zustand: Brand New. 411 pages. 9.25x6.10x9.21 inches. In Stock.

Hardcover. Zustand: Brand New. 446 pages. 9.25x6.10x9.21 inches. In Stock.