Query by Transduction

There has been recently a growing interest in the use of transductive inference for learning. We expand here the scope of transductive inference to active learning in a stream-based setting. Towards that end this paper proposes Query-by-Transduction (QBT) as a novel active learning algorithm. QBT qu...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 30; no. 9; pp. 1557 - 1571
Main Authors: Shen-Shyang Ho, Wechsler, H.
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01.09.2008
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Bayesian methods
Classification
Computer science; control theory; systems
Data processing. List processing. Character string processing
Exact sciences and technology
Feasibility
Frequency
Inference
Inference algorithms
Information Storage and Retrieval - methods
Information systems. Data bases
Labeling
Learning
Machine learning
Mathematics
Memory organisation. Data processing
Pattern analysis
Pattern Recognition, Automated - methods
Probability and statistics
Query processing
Sampling methods
Sampling theory, sample surveys
Sciences and techniques of general use
Sensitivity and Specificity
Software
Statistical
Statistical analysis
Statistics
Support vector machine classification
Support vector machines
Tasks
Testing
Utilities
Statistical
Machine learning
Bayes estimation
Statistical analysis
Divergence
Random sampling
Database query
hypothesis testing
Active learning
Inference
Shannon theory
transductive inference
Hypothesis test
Statistical test
support vector machine
Active system
Kolmogorov complexity
Vector support machine
Feasibility
Learning algorithm
Pattern analysis
Kolmogorov equation
Artificial intelligence
Multiclass
ISSN:0162-8828, 1939-3539
Online Access:Get full text
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Description
Summary:There has been recently a growing interest in the use of transductive inference for learning. We expand here the scope of transductive inference to active learning in a stream-based setting. Towards that end this paper proposes Query-by-Transduction (QBT) as a novel active learning algorithm. QBT queries the label of an example based on the p-values obtained using transduction. We show that QBT is closely related to Query-by-Committee (QBC) using relations between transduction, Bayesian statistical testing, Kullback-Leibler divergence, and Shannon information. The feasibility and utility of QBT is shown on both binary and multi-class classification tasks using SVM as the choice classifier. Our experimental results show that QBT compares favorably, in terms of mean generalization, against random sampling, committee-based active learning, margin-based active learning, and QBC in the stream-based setting.
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ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2007.70811