unsupervised learning algorithms (25 Ergebnisse)

Zustand: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Paperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Zustand: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.

Soft cover. Zustand: New. Concepts of Machine Learning with Practical Approaches. KEY FEATURES Includes real-scenario examples to explain the working of Machine Learning algorithms. Includes graphical and statistical representation to simplify modeling Machine Learning and Neural Networks. Full of Python codes, numerous exercises, and model question papers for data science students. DESCRIPTION The book offers the readers the fundamental concepts of Machine Learning techniques in a user-friendly language. The book aims to give in-depth knowledge of the different Machine Learning (ML) algorithms and the practical implementation of the various ML approaches. This book covers different Supervised Machine Learning algorithms such as Linear Regression Model, Naïve Bayes classifier Decision Tree, K-nearest neighbor, Logistic Regression, Support Vector Machine, Random forest algorithms, Unsupervised Machine Learning algorithms such as k-means clustering, Hierarchical Clustering, Probabilistic clustering, Association rule mining, Apriori Algorithm, f-p growth algorithm, Gaussian mixture model and Reinforcement Learning algorithm such as Markov Decision Process (MDP), Bellman equations, policy evaluation using Monte Carlo, Policy iteration and Value iteration, Q-Learning, State-Action-Reward-State-Action (SARSA). It also includes various feature extraction and feature selection techniques, the Recommender System, and a brief overview of Deep Learning. By the end of this book, the reader can understand Machine Learning concepts and easily implement various ML algorithms to real-world problems.

Zustand: New. In.

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Taschenbuch. Zustand: Neu. Neuware.

Taschenbuch. Zustand: Neu. Machine Learning | Supervised,Unsupervised Learning,Ensemble and Probabilistic,Reinforcement Learning,Genetic Algorithms | G. Krishna Kumari | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204201092 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Zustand: New. In English.

Zustand: New. In.

Zustand: New. In.

Taschenbuch. Zustand: Neu. Novel Approach for Single Channel Blind Source Separation | Unsupervised Learning Algorithms and Applications | Bin Gao (u. a.) | Taschenbuch | 192 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659260001 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Paperback. Zustand: Brand New. 192 pages. 8.66x0.44x5.91 inches. In Stock.

Paperback. Zustand: Brand New. 260 pages. 9.00x6.00x0.63 inches. In Stock.

Taschenbuch. Zustand: Neu. Kernel Based Algorithms for Mining Huge Data Sets | Supervised, Semi-supervised, and Unsupervised Learning | Te-Ming Huang (u. a.) | Taschenbuch | Studies in Computational Intelligence | xvi | Englisch | 2010 | Springer | EAN 9783642068560 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 'Kernel Based Algorithms for Mining Huge Data Sets' is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction (feature elimination) and shows the similarities and differences between the two most popular unsupervised techniques, the principal component analysis (PCA) and the independent component analysis (ICA). The book presents various examples, software, algorithmic solutions enabling the reader to develop their own codes for solving the problems. The book is accompanied by a website for downloading both data and software for huge data sets modeling in a supervised and semisupervised manner, as well as MATLAB based PCA and ICA routines for unsupervised learning. The book focuses on a broad range of machine learning algorithms and it is particularly aimed at students, scientists, and practicing researchers in bioinformatics (gene microarrays), text-categorization, numerals recognition, as well as in the images and audio signals de-mixing (blind source separation) areas.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 'Kernel Based Algorithms for Mining Huge Data Sets' is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction (feature elimination) and shows the similarities and differences between the two most popular unsupervised techniques, the principal component analysis (PCA) and the independent component analysis (ICA). The book presents various examples, software, algorithmic solutions enabling the reader to develop their own codes for solving the problems. The book is accompanied by a website for downloading both data and software for huge data sets modeling in a supervised and semisupervised manner, as well as MATLAB based PCA and ICA routines for unsupervised learning. The book focuses on a broad range of machine learning algorithms and it is particularly aimed at students, scientists, and practicing researchers in bioinformatics (gene microarrays), text-categorization, numerals recognition, as well as in the images and audio signals de-mixing (blind source separation) areas.

Zustand: New. In.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how withthe proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest includeanomaly detection, clustering, feature extraction, and applications of unsupervised learning. Each chapter is contributed by a leading expert in the field.

Hardcover. Zustand: Brand New. 558 pages. French language. 9.50x6.50x1.50 inches. In Stock.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how withthe proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest includeanomaly detection, clustering, feature extraction, and applications of unsupervised learning. Each chapter is contributed by a leading expert in the field.

Zustand: fine. l'article peut presenter de tres legers signes d'usure, petites rayures ou imperfections esthetiques. vendeur professionnel; envoi soigne en 24/48h.

Zustand: New.