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Feature Engineering for Machine Learning and Data Analytics.

Author: Guozhu Dong; Huan Liu
Publisher: Milton : Chapman and Hall/CRC, 2018.
Series: Chapman and Hall/CRC Data Mining and Knowledge Discovery Ser.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Dong, Guozhu.
Feature Engineering for Machine Learning and Data Analytics.
Milton : Chapman and Hall/CRC, ©2018
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Guozhu Dong; Huan Liu
ISBN: 9781351721264 1351721267
OCLC Number: 1029486965
Notes: 5.5.4 Unsupervised Streaming Feature Selection in Social Media.
Description: 1 online resource (340 pages)
Contents: Intro; Halftitle Page; Title Page; Copyright; Table of Contents; Preface; Contributors; 1 Preliminaries and overview; 1.1 Preliminaries; 1.1.1 Features; 1.1.2 Feature engineering; 1.1.3 Machine learning and data analytic tasks; 1.2 Overview of the chapters; 1.3 Beyond this book; 1.3.1 Feature engineering for specific data types; 1.3.2 Feature engineering on non-data-specific topics; PART I Feature Engineering for Various Data Types; 2 Feature Engineering for Text Data; 2.1 Introduction; 2.2 Overview of Text Representation; 2.3 Text as Strings; 2.4 Sequence of Words Representation. 2.5 Bag of Words Representation2.5.1 Term Weighting; 2.5.2 Beyond Single Words; 2.6 Structural Representation of Text; 2.6.1 Semantic Structure Features; 2.7 Latent Semantic Representation; 2.7.1 Latent Semantic Analysis; 2.7.2 Probabilistic Latent Semantic Analysis; 2.7.3 Latent Dirichlet Allocation; 2.8 Explicit Semantic Representation; 2.9 Embeddings for Text Representation; 2.9.1 Matrix Factorization for Word Embeddings; 2.9.2 Neural Networks for Word Embeddings; 2.9.3 Document Representations from Word Embeddings; 2.10 Context-Sensitive Text Representation; 2.11 Summary. 3 Feature Extraction and Learning for Visual Data3.1 Classical Visual Feature Representations; 3.1.1 Color Features; 3.1.2 Texture Features; 3.1.3 Shape Features; 3.2 Latent Feature Extraction; 3.2.1 Principal Component Analysis; 3.2.2 Kernel Principal Component Analysis; 3.2.3 Multidimensional Scaling; 3.2.4 Isomap; 3.2.5 Laplacian Eigenmaps; 3.3 Deep Image Features; 3.3.1 Convolutional Neural Networks; 3.3.1.1 The Dot-Product Layer; 3.3.1.2 The Convolution Layer; 3.3.2 CNN Architecture Design; 3.3.3 Fine-Tuning Off-the-Shelf Neural Networks; 3.3.4 Summary and Conclusions. 4 Feature-Based Time-Series Analysis4.1 Introduction; 4.1.1 The Time Series Data Type; 4.1.2 Time-Series Characterization; 4.1.3 Applications of Time-Series Analysis; 4.2 Feature-Based Representations of Time Series; 4.3 Global Features; 4.3.1 Examples of Global Features; 4.3.2 Massive Feature Vectors and Highly Comparative Time-Series Analysis; 4.4 Subsequence Features; 4.4.1 Interval Features; 4.4.2 Shapelets; 4.4.3 Pattern Dictionaries; 4.5 Combining Time-Series Representations; 4.6 Feature-Based Forecasting; 4.7 Summary and Outlook; 5 Feature Engineering for Data Streams; 5.1 Introduction. 5.2 Streaming Settings5.3 Linear Methods for Streaming Feature Construction; 5.3.1 Principal Component Analysis for Data Streams; 5.3.2 Linear Discriminant Analysis for Data Streams; 5.4 Nonâ#x80;#x90;Linear Methods for Streaming Feature Construction; 5.4.1 Locally Linear Embedding for Data Streams; 5.4.2 Kernel Learning for Data Streams; 5.4.3 Neural Networks for Data Streams; 5.4.4 Discussion; 5.5 Feature Selection for Data Streams with Streaming Features; 5.5.1 The Grafting Algorithm; 5.5.2 The Alphaâ#x80;#x90;Investing Algorithm; 5.5.3 The Online Streaming Feature Selection Algorithm.
Series Title: Chapman and Hall/CRC Data Mining and Knowledge Discovery Ser.

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