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Compressed classification learning with Markov chain samples.

Feilong Cao¹, Tenghui Dai¹, Yongquan Zhang¹

¹Department of Information and Mathematics Sciences, China Jiliang University, Hangzhou 310018, Zhejiang Province, PR China.

Neural Networks : the Official Journal of the International Neural Network Society

|December 11, 2013

Summary

This summary is machine-generated.

Compressed classification learning with Support Vector Machines (SVMs) offers near-optimal accuracy while mitigating the curse of dimensionality. This method efficiently reduces learning time, with controllable trade-offs in classification accuracy.

Keywords:

Compressed classification Generalization error Markov chain samples SVM

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Area of Science:

Machine Learning
Data Science
Pattern Recognition

Background:

Compressed classification learning is an emerging area focused on reducing computational complexity.
Support Vector Machines (SVMs) are powerful algorithms for classification tasks.
The curse of dimensionality poses a significant challenge in high-dimensional data analysis.

Purpose of the Study:

To establish a generalization bound for SVMs in compressed classification.
To demonstrate how compressed learning can circumvent the curse of dimensionality.
To analyze the trade-off between learning time and classification accuracy in compressed learning.

Main Methods:

Derivation of a generalization bound for SVMs using uniformly ergodic Markov chain samples.
Theoretical analysis of the impact of compression on classification accuracy and learning time.
Empirical validation through numerical experiments.

Main Results:

A generalization bound showing SVM compressed classification accuracy approaches that of the data domain.
Evidence that compressed learning avoids the curse of dimensionality.
Demonstration that compressed learning reduces learning time with controllable accuracy reduction.

Conclusions:

Compressed classification learning, particularly with SVMs, is a viable approach for efficient high-dimensional data analysis.
The established bounds provide theoretical support for the effectiveness of compressed SVMs.
Controlled trade-offs allow for practical applications where learning time is critical.