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Machine Learning Techniques for Text: Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluatio
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Machine Learning Techniques for Text: Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation
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Machine Learning Techniques for Text : Apply Modern Techniques with Python for Text Processing, Dimensionality Reduction, Classification, and Evaluation
Published by Packt Publishing, Limited, 2022
ISBN 10: 1803242388 ISBN 13: 9781803242385
Language: English
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Machine Learning Techniques for Text: Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluatio
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Machine Learning Techniques for Text: Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluatio (Paperback or Softback)
Published by Packt Publishing 10/31/2022, 2022
ISBN 10: 1803242388 ISBN 13: 9781803242385
Language: English
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Add to basketPaperback or Softback. Condition: New. Machine Learning Techniques for Text: Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluatio. Book.
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Machine Learning Approach: For dimensionality reduction of microarray data
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 620056843X ISBN 13: 9786200568434
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Machine Learning Approach: For dimensionality reduction of microarray data
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 620056843X ISBN 13: 9786200568434
Language: English
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Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Published by Chapman and Hall/CRC 2013-12-11, 2013
ISBN 10: 1439857245 ISBN 13: 9781439857243
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Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Published by Chapman and Hall/CRC, 2013
ISBN 10: 1439857245 ISBN 13: 9781439857243
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Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Published by Chapman and Hall/CRC, 2013
ISBN 10: 1439857245 ISBN 13: 9781439857243
Language: English
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Dimensionality Reduction in Machine Learning (Paperback)
Published by Elsevier Science & Technology, San Francisco, 2025
ISBN 10: 0443328188 ISBN 13: 9780443328183
Language: English
Paperback. Condition: new. Paperback. Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding.Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Published by Chapman and Hall/CRC, 2013
ISBN 10: 1439857245 ISBN 13: 9781439857243
Language: English
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Dimensionality Reduction in Machine Learning (Paperback)
Published by Elsevier Science & Technology, San Francisco, 2025
ISBN 10: 0443328188 ISBN 13: 9780443328183
Language: English
£ 165.53
Convert currency£ 37.58 shipping from U.S.A. to United KingdomQuantity: 1 available
Add to basketPaperback. Condition: new. Paperback. Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding.Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Condition: Brand New. 250 pages. 9.25x7.50x9.22 inches. In Stock.
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Dimensionality Reduction in Machine Learning
Published by Elsevier Science Feb 2025, 2025
ISBN 10: 0443328188 ISBN 13: 9780443328183
Language: English
£ 222.03
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Add to basketTaschenbuch. Condition: Neu. Neuware - Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding.Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data.
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paperback. Condition: New. New. book.
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Machine Learning Techniques for Text: Apply modern Python techniques for text preprocessing, dimensionality reduction and performance
Published by Packt Publishing Limited, 2022
ISBN 10: 1803242388 ISBN 13: 9781803242385
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
£ 44.16
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Add to basketPaperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 100.
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Machine Learning Approach : For dimensionality reduction of microarray data
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 620056843X ISBN 13: 9786200568434
Language: English
£ 45.23
Convert currency£ 12.16 shipping from Germany to United KingdomQuantity: 1 available
Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - For past several years, microarray technology has attracted tremendous interest for both scientific community and industry. Recently, the applications of microarrays include gene discovery, disease diagnosis and prognosis, drug discovery, etc. High dimensional data with small sample size is the main problem that generate the application of dimension reduction in microarray data analysis. It is seen that SVM, ANN and NB have recently gained wide popularity for cancer classification problems. An efficient and reliable method of dimension reduction plays an important role to improve the performance of SVM, ANN and NB, when applied for classification of high dimensional microarray data. In this book, we applied different combinations of feature selection / extraction methods, as a novel hybrid dimension reduction method for SVM, ANN and NB classifiers. The obtained results are compared with other popular published dimension reduction methods for SVM, NB and ANN classifiers.
