10.1162/153244302760185243
Applied Physics LettersPublished 1 January 2000Open access
Citations898
SJR quartileQ1
SJR score0.90
SNIP1.00
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Experimental results showing that employing the active learning method can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings are presented.
Abstract
Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using pool-based active learning. Instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels for some number of them. We introduce a new algorithm for performing active learning with support vector machines, i.e., an algorithm for choosing which instances to request next. We provide a theoretical motivation for the algorithm using the notion of a version space. We present experimental results showing that employing our active learning method can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.
Keywords
Arts and HumanitiesHealth Professions
Data Mining and Knowledge DiscoveryA Tutorial on Support Vector Machines for Pattern Recognition
16,433 Citations1998Christopher J. C. Burges
There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Lecture notes in computer scienceText categorization with Support Vector Machines: Learning with many relevant features
7,925 Citations1998Thorsten Joachims
SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of di-erent learning tasks, eliminating the need for manual parameter tuning.
Technical reportsMaking Large-Scale SVM Learning Practical
4,317 Citations2006Thorsten Joachims
This chapter presents algorithmic and computational results developed for SVM light V 2.0, which make large-scale SVM training more practical and give guidelines for the application of SVMs to large domains.
Transductive Inference for Text Classification using Support Vector Machines
2,717 Citations1999Thorsten Joachims
An analysis of why TSVMs are well suited for text classi(cid:12)cation is presented, and an algorithm for training TSVMs e(cid:14)-ciently, handling 10,000 examples and more is proposed.
Information science and statisticsEstimation of Dependences Based on Empirical Data
2,238 Citations2006Vladimir Vapnik
Elsevier eBooksNewsWeeder: Learning to Filter Netnews
2,022 Citations1995Ken Lang
The results show that a learning algorithm based on the Minimum Description Length (MDL) principle was able to raise the percentage of interesting articles to be shown to users from 14% to 52% on average.
Query by committee
1,657 Citations1992H. Sebastian Seung, Manfred Opper +1 more
It is suggested that asymptotically finite information gain may be an important characteristic of good query algorithms, in which a committee of students is trained on the same data set.
Inductive learning algorithms and representations for text categorization
1,465 Citations1998Susan Dumais, John Platt +2 more
A comparison of the effectiveness of five different automatic learning algorithms for text categorization in terms of learning speed, realtime classification speed, and classification accuracy is compared.
A Sequential Algorithm for Training Text Classifiers
1,158 Citations1994David Lewis, William A. Gale
An algorithm for sequential sampling during machine learning of statistical classifiers was developed and tested on a newswire text categorization task and reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness.
Incremental and Decremental Support Vector Machine Learning
1,158 Citations2000Gert Cauwenberghs, Tomaso Poggio
An on-line recursive algorithm for training support vector machines, one vector at a time, is presented and interpretation of decremental unlearning in feature space sheds light on the relationship between generalization and geometry of the data.
Elsevier eBooksHeterogeneous Uncertainty Sampling for Supervised Learning
1,142 Citations1994David Lewis, Jason Catlett
This work test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program) and finds that the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger.
Machine LearningSelective Sampling Using the Query by Committee Algorithm
1,118 Citations1997Yoav Freund, H. Sebastian Seung +2 more
It is shown that if the two-member committee algorithm achieves information gain with positive lower bound, then the prediction error decreases exponentially with the number of queries, and this exponential decrease holds for query learning of perceptrons.
Less is More: Active Learning with Support Vector Machines
775 Citations2000Greg Schohn, David Cohn
A simple active learning heuristic is described which greatly enhances the generalization behavior of support vector machines (SVMs) on several practical document classification tasks and frequently does so in less time than the naive approach of training on all available data.
Elsevier eBooksCommittee-Based Sampling For Training Probabilistic Classifiers
497 Citations1995Ido Dagan, Sean P. Engelson
A general method for efficiently training probabilistic classifiers, by selecting for training only the more informative examples in a stream of unlabeled examples, which is particularly attractive because it evaluates the expected information gain from a training example implicitly.
PAC-Bayesian model averaging
372 Citations1999David McAllester
The method constructs an optimized weighted mixture of concepts analogous to a Bayesian posterior distribution, and the main result is stated for bounded loss, a preliminary analysis for unbounded loss is also given.
Elsevier eBooksTime-Dependent Utility and Action Under Uncertainty
81 Citations1991Eric Horvitz, Geoffrey W. Rutledge
This work presents a semantics for time-dependent utility and describes the use of time- dependent information in decision contexts and discusses representing and reasoning with knowledge about the time-independent utility of an agent's actions.
Further results on the margin distribution
64 Citations1999John Shawe‐Taylor, Nello Cristianini
It is shown that in the linear case the approach can be viewed as a change of kernel and that the algorithms arising from the approach are exactly those originally proposed by Cortes and Vapnik.