𝔖 Scriptorium
✦   LIBER   ✦

πŸ“

Semi-Supervised Learning

✍ Scribed by Olivier Chapelle, Bernhard Schâlkopf, Alexander Zien


Publisher
MIT Press
Year
2006
Tongue
English
Leaves
524
Series
Adaptive computation and machine learning
Category
Library

⬇  Acquire This Volume

No coin nor oath required. For personal study only.

✦ Synopsis


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.


πŸ“œ SIMILAR VOLUMES


Semi-Supervised Learning
✍ Olivier Chapelle, Bernhard Scholkopf, Alexander Zien πŸ“‚ Library πŸ“… 2006 πŸ› The MIT Press 🌐 English

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 i

Semi-Supervised Learning
✍ Olivier Chapelle, Bernhard SchΓΆlkopf, Alexander Zien πŸ“‚ Library πŸ“… 2006 πŸ› The MIT Press 🌐 English

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 a

Graph-Based Semi-Supervised Learning
✍ Amarnag Subramanya, Partha Pratim Talukdar πŸ“‚ Library πŸ“… 2014 πŸ› Morgan & Claypool Publishers 🌐 English

<p>While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line

Introduction to Semi-Supervised Learning
✍ Xiaojin Zhu, Andrew. B Goldberg πŸ“‚ Library πŸ“… 2009 πŸ› Springer 🌐 English

<span>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 detecti

Graph-Based Semi-Supervised Learning
✍ Amarnag Subramanya, Partha Pratim Talukdar πŸ“‚ Library πŸ“… 2014 πŸ› Springer 🌐 English

<span>While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate li