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About 10,000 papers for “Semi-supervised learning”

Semi-Supervised Learning

Uday Shankar Shanthamallu, Andreas Spanias · Synthesis lectures on signal processing · 2022

Cited by 5

Semi-Supervised Learning

Khalid M. Mosalam, Yuqing Gao · Synthesis lectures on mechanical engineering · 2024

Cited by 12

Semi-supervised Learning

Mohamed Farouk Abdel Hady, Friedhelm Schwenker · Intelligent systems reference library · 2013

Cited by 124

Semi-Supervised Learning

Olivier Chapelle, Bernhard Schlkopf, Alexander Zien · The MIT Press eBooks · 2006

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.

Cited by 4,285Open access

Semi-Supervised Learning

Tobias Scheffer · IGI Global eBooks · 2005

For many classification problems, unlabeled training data are inexpensive and readily available, whereas labeling training data imposes costs. Semi-supervised classification algorithms aim at utilizing information contained in unlabeled data in addition to the (few) labeled data.

Cited by 629

Safe Semi-Supervised Learning

Andrea Bertazzi · Research Repository (Delft University of Technology) · 2018

Semi-supervised algorithms have been shown to possibly have a worse performance than the corresponding supervised model. This may be due to a violation of the assumptions on the data that are introduced in most classification systems. We study an approach that was previously shown to have guarantees of improvement for the LDA classifier in terms of log-likelihood on the full data-set of labeled and unlabeled observations. This method is based on two key concepts: contrast and pessimism. We extend this approach to a broader class of probabilistic generative models, in which the class conditional distributions can be modeled with any parametric class that belongs to the exponential family. In this case, we prove that the classifier is never worse and, under mild assumptions, strictly improves the log-likelihood on the complete data-set. The case of Gaussian densities is analyzed in detail, both for LDA and QDA. Moreover, we study this method in the case of least squares classification. In terms of square loss we prove the contrastive pessimistic classifier is guaranteed not to degrade the performance and, with a further requirement on the data, it strictly outperforms the supervised model. <br/>Finally, we apply contrast and pessimism to the task of parameter estimation of a multivariate Gaussian density in a missing data framework. We fully characterize the case of a missing block of data and we show that a strictly increased likelihood on the complete data-set is obtained for any monotone sample. In other terms, the contrastive pessimistic estimates are guaranteed to fit better the complete data-set composed of both observed and hidden components.

Cited by 2Open access