Download Semi-Supervised Learning by Olivier Chapelle, Bernhard Schölkopf, Alexander Zien PDF

By Olivier Chapelle, Bernhard Schölkopf, Alexander Zien

Within the box of desktop studying, semi-supervised studying (SSL) occupies the center flooring, among supervised studying (in which all education examples are classified) and unsupervised studying (in which no label information are given). curiosity in SSL has elevated in recent times, fairly as a result of software domain names within which unlabeled information are considerable, similar to photos, textual content, and bioinformatics. this primary finished review of SSL provides state of the art algorithms, a taxonomy of the sector, chosen functions, benchmark experiments, and views on ongoing and destiny research.Semi-Supervised studying first offers the most important assumptions and concepts underlying the sphere: smoothness, cluster or low-density separation, manifold constitution, and transduction. The center of the e-book is the presentation of SSL equipment, prepared in response to algorithmic innovations. After an exam of generative types, the booklet describes algorithms that enforce the low-density separation assumption, graph-based tools, and algorithms that practice two-step studying. The ebook then discusses SSL purposes and provides instructions for SSL practitioners by means of studying the result of wide benchmark experiments. eventually, the e-book seems at fascinating instructions for SSL learn. The ebook closes with a dialogue of the connection among semi-supervised studying and transduction.Olivier Chapelle and Alexander Zien are examine Scientists and Bernhard Schölkopf is Professor and Director on the Max Planck Institute for organic Cybernetics in Tübingen. Schölkopf is coauthor of studying with Kernels (MIT Press, 2002) and is a coeditor of Advances in Kernel tools: help Vector studying (1998), Advances in Large-Margin Classifiers (2000), and Kernel tools in Computational Biology (2004), all released through The MIT Press.

Show description

Read or Download Semi-Supervised Learning PDF

Similar machine theory books

Data Integration: The Relational Logic Approach

Info integration is a serious challenge in our more and more interconnected yet unavoidably heterogeneous international. there are lots of info resources on hand in organizational databases and on public details structures just like the world-wide-web. no longer strangely, the resources usually use various vocabularies and diversified info constructions, being created, as they're, by means of diversified humans, at diverse instances, for various reasons.

Approximation, Randomization, and Combinatorial Optimization: Algorithms and Techniques: 4th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2001 and 5th International Workshop on Randomization and Approx

This publication constitutes the joint refereed lawsuits of the 4th foreign Workshop on Approximation Algorithms for Optimization difficulties, APPROX 2001 and of the fifth foreign Workshop on Ranomization and Approximation recommendations in machine technological know-how, RANDOM 2001, held in Berkeley, California, united states in August 2001.

Relational and Algebraic Methods in Computer Science: 15th International Conference, RAMiCS 2015 Braga, Portugal, September 28 – October 1, 2015, Proceedings

This ebook constitutes the complaints of the fifteenth overseas convention on Relational and Algebraic tools in laptop technology, RAMiCS 2015, held in Braga, Portugal, in September/October 2015. The 20 revised complete papers and three invited papers awarded have been rigorously chosen from 25 submissions. The papers take care of the idea of relation algebras and Kleene algebras, method algebras; mounted element calculi; idempotent semirings; quantales, allegories, and dynamic algebras; cylindric algebras, and approximately their program in parts resembling verification, research and improvement of courses and algorithms, algebraic methods to logics of courses, modal and dynamic logics, period and temporal logics.

Biometrics in a Data Driven World: Trends, Technologies, and Challenges

Biometrics in an information pushed global: traits, applied sciences, and demanding situations goals to notify readers in regards to the smooth purposes of biometrics within the context of a data-driven society, to familiarize them with the wealthy heritage of biometrics, and to supply them with a glimpse into the way forward for biometrics.

Extra resources for Semi-Supervised Learning

Sample text

These are the assumptions used by the naive Bayes classifier, a commonly used tool for standard supervised text categorization (Lewis, 1998; McCallum and Nigam, 1998a). We assume documents are generated by a mixture of multinomials model, where each mixture component corresponds to a class. Let there be M classes and a vocabulary of size |X|; each document xi has |xi | words in it. How do we create a document using this model? First, we roll a biased M -sided die to determine the class of our document.

This is somewhat confirmed by the weak results in (Tong and Koller, 2000). , 2001) in order to modify the diagnostic SVM framework. Anderson (Anderson, 1979) suggested an interesting modification of logistic regression in which unlabeled data can be used. In binary logistic regression, the log odds are modeled as linear function, which gives P (x|1) = exp(β T x)P (x|2) and P (x) = (π1 exp(β T x)+1−π1 )P (x|2), where π1 = P {t = 1}. Anderson now chooses the parameters β, π1 and P (x|2) in order to maximize the likelihood of both Dl and Du , subject to the constraints that P (x|1) and P (x|2) are normalized.

If document xi was generated by mixture component cj we say yi = cj . A document, xi , is a vector of word counts. We write xit to be the number of times word wt occurs in document xi . When a document is to be generated by a |X| particular mixture component a document length, |xi | = t=1 xit , is first chosen 2 independently of the component. Then, the selected mixture component is used to generate a document of the specified length, by drawing from its multinomial distribution. 3 P(xi |cj ; θ) ∝ P(|xi |) P(wt |cj ; θ)xit .

Download PDF sample

Rated 4.88 of 5 – based on 11 votes