Author: Alfred Lawrence Hall-QuestPublisher:
ISBN:
Category :
Languages : en
Pages : 478
Book Description
Supervised study
Author: Alfred Lawrence Hall-QuestPublisher:
ISBN:
Category :
Languages : en
Pages : 478
Book Description
Supervised Study; a Discussion of the Study Lesson in High School
Author: Alfred Lawrence Hall-QuestPublisher:
ISBN:
Category : High schools
Languages : en
Pages : 460
Book Description
A Study of Supervised Study
Author: University of Illinois (Urbana-Champaign campus). Bureau of Educational ResearchPublisher:
ISBN:
Category : Education
Languages : en
Pages : 54
Book Description
Supervised Study in American History
Author: Mabel Elizabeth SimpsonPublisher:
ISBN:
Category : United States
Languages : en
Pages : 304
Book Description
Supervised Study in the Elementary School
Author: Alfred Lawrence Hall-QuestPublisher:
ISBN:
Category : Education
Languages : en
Pages : 496
Book Description
Supervised Study in Mathematics and Science
Author: Stephen Clayton SumnerPublisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 266
Book Description
Semi-Supervised Learning
Author: Olivier ChapellePublisher: MIT Press
ISBN: 0262514125
Category : Computers
Languages : en
Pages : 525
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.
Introduction to Semi-Supervised Learning
Author: Xiaojin GeffnerPublisher: Springer Nature
ISBN: 3031015487
Category : Computers
Languages : en
Pages : 116
Book Description
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and Outlook
Supervised Study in English for Junior High School Grades
Author: Anne Laura McGregorPublisher:
ISBN:
Category : English language
Languages : en
Pages : 250
Book Description
Supervised Study Plan of Teaching
Author: Francis ShrevePublisher:
ISBN:
Category : Study skills
Languages : en
Pages : 568
Book Description