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Neural net algorithms that learn in polynomial time from examples and queries.

¹NEC Res. Inst., Princeton, NJ.

IEEE Transactions on Neural Networks

|January 1, 1991

Summary

A new algorithm efficiently trains neural networks using examples and expert queries. It is proven to PAC learn specific network types in polynomial time, showing practical effectiveness for complex machine learning tasks.

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Area of Science:

Machine Learning
Computational Neuroscience
Artificial Intelligence

Background:

Training neural networks often requires extensive labeled data.
Query-based learning offers an alternative by using an oracle for specific data points.
Threshold logic networks are fundamental models in computational learning theory.

Purpose of the Study:

To introduce and analyze a novel algorithm for training neural networks using examples and queries.
To establish the computational complexity and learning capabilities of the proposed algorithm.
To explore the algorithm's applicability to various network architectures and mathematical problems.

Main Methods:

The algorithm utilizes an oracle to obtain target function values for queried inputs.
PAC (Probably Approximately Correct) learning framework is employed to analyze the algorithm's efficiency.
The algorithm's performance is theoretically analyzed for depth-two threshold nets and intersections of half-spaces.
Empirical tests were conducted on a variant of the algorithm with random networks.

Main Results:

The algorithm achieves polynomial-time PAC learnability for depth-two threshold nets with k <= 4 hidden units.
It can also learn intersections of k half-spaces in R(n) in polynomial time.
A variant demonstrates efficient learning of arbitrary depth layered threshold networks.
Experimental results show rapid and consistent learning of random networks.

Conclusions:

The proposed query-based algorithm offers an efficient method for training specific types of neural networks.
The theoretical guarantees and practical performance suggest its utility in machine learning applications.
The algorithm provides a valuable tool for computational learning theory and AI research.