Semi-supervised learning /
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Imprint: | Cambridge, Mass. : MIT Press, ©2006. |
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Description: | 1 online resource (x, 508 pages) : illustrations |
Language: | English |
Series: | Adaptive computation and machine learning Adaptive computation and machine learning. |
Subject: | |
Format: | E-Resource Book |
URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11148728 |
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245 | 0 | 0 | |a Semi-supervised learning / |c [edited by] Olivier Chapelle, Bernhard Schölkopf, Alexander Zien. |
260 | |a Cambridge, Mass. : |b MIT Press, |c ©2006. | ||
300 | |a 1 online resource (x, 508 pages) : |b illustrations | ||
336 | |a text |b txt |2 rdacontent | ||
337 | |a computer |b c |2 rdamedia | ||
338 | |a online resource |b cr |2 rdacarrier | ||
347 | |a data file |2 rda | ||
490 | 1 | |a Adaptive computation and machine learning | |
504 | |a Includes bibliographical references (pages 479-497). | ||
588 | 0 | |a Print version record. | |
520 | 8 | |a A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems, this text looks at state-of-the-art algorithms, applications benchmark experiments, and directions for future research. | |
505 | 0 | |a Series Foreword; Preface; 1 -- Introduction to Semi-Supervised Learning; 2 -- A Taxonomy for Semi-Supervised Learning Methods; 3 -- Semi-Supervised Text Classification Using EM; 4 -- Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers; 5 -- Probabilistic Semi-Supervised Clustering with Constraints; 6 -- Transductive Support Vector Machines; 7 -- Semi-Supervised Learning Using Semi- Definite Programming; 8 -- Gaussian Processes and the Null-Category Noise Model; 9 -- Entropy Regularization; 10 -- Data-Dependent Regularization. | |
505 | 8 | |a 11 -- Label Propagation and Quadratic Criterion12 -- The Geometric Basis of Semi-Supervised Learning; 13 -- Discrete Regularization; 14 -- Semi-Supervised Learning with Conditional Harmonic Mixing; 15 -- Graph Kernels by Spectral Transforms; 16- Spectral Methods for Dimensionality Reduction; 17 -- Modifying Distances; 18 -- Large-Scale Algorithms; 19 -- Semi-Supervised Protein Classification Using Cluster Kernels; 20 -- Prediction of Protein Function from Networks; 21 -- Analysis of Benchmarks; 22 -- An Augmented PAC Model for Semi- Supervised Learning. | |
505 | 8 | |a 23 -- Metric-Based Approaches for Semi- Supervised Regression and Classification24 -- Transductive Inference and Semi-Supervised Learning; 25 -- A Discussion of Semi-Supervised Learning and Transduction; References; Notation and Symbols; Contributors; Index. | |
546 | |a English. | ||
650 | 0 | |a Supervised learning (Machine learning) |0 http://id.loc.gov/authorities/subjects/sh94008290 | |
650 | 7 | |a COMPUTERS |x Enterprise Applications |x Business Intelligence Tools. |2 bisacsh | |
650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics. |2 bisacsh | |
650 | 7 | |a Supervised learning (Machine learning) |2 fast |0 (OCoLC)fst01139041 | |
653 | |a COMPUTER SCIENCE/Machine Learning & Neural Networks | ||
655 | 0 | |a Electronic books. | |
655 | 4 | |a Electronic books. | |
700 | 1 | |a Chapelle, Olivier. | |
700 | 1 | |a Schölkopf, Bernhard. | |
700 | 1 | |a Zien, Alexander. | |
776 | 0 | 8 | |i Print version: |t Semi-supervised learning. |d Cambridge, Mass. : MIT Press, ©2006 |z 0262033585 |w (DLC) 2006044448 |w (OCoLC)64898359 |
830 | 0 | |a Adaptive computation and machine learning. |0 http://id.loc.gov/authorities/names/n97066095 | |
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