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Author: Alfred Lawrence Hall-Quest
Publisher:
ISBN:
Category : High schools
Languages : en
Pages : 472
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Book Description
Author: Alfred Lawrence Hall-Quest
Publisher:
ISBN:
Category : High schools
Languages : en
Pages : 472
Get Book Here
Book Description
Author: William Arthur Brownell
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Alfred Lawrence Hall-Quest
Publisher:
ISBN:
Category : High schools
Languages : en
Pages : 460
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Book Description
Author: Frances Charlotte Cuatt
Publisher:
ISBN:
Category :
Languages : en
Pages : 64
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Book Description
Author: Olivier Chapelle
Publisher: MIT Press
ISBN: 0262514125
Category : Computers
Languages : en
Pages : 525
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Book Description
A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.
Author: H. L. Miller
Publisher:
ISBN:
Category :
Languages : en
Pages : 32
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Book Description
Author: Alfred Lawrence Hall-Quest
Publisher:
ISBN:
Category :
Languages : en
Pages : 478
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Book Description
Author: Alfred Lawrence Hall-Quest
Publisher:
ISBN:
Category : Education, Elementary
Languages : en
Pages : 496
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Book Description
A discussion and program of supervised study in language, arithmetic, social sciences and other subjects for elementary school students.
Author: William A (William Arthur) Brownell
Publisher: Hassell Street Press
ISBN: 9781014755162
Category :
Languages : en
Pages : 56
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Book Description
This work has been selected by scholars as being culturally important and is part of the knowledge base of civilization as we know it. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. To ensure a quality reading experience, this work has been proofread and republished using a format that seamlessly blends the original graphical elements with text in an easy-to-read typeface. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Author: Emil Hvitfeldt
Publisher: CRC Press
ISBN: 1000461971
Category : Computers
Languages : en
Pages : 402
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Book Description
Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.
