Publication

On the Complexity of the Class of Regions Computable by a Two-Layered Perceptron

1996
Reports

Abstract

This work is concerned with the computational complexity of the recognition of $\mbox{LP}_2$ , the class of regions of the Euclidian space that can be classified exactly by a two-layered perceptron. Several subclasses of $\mbox{LP}_2$ of particular interest are also considered. We show that the recognition problems of $\mbox{LP}_2$ and of other classes considered here are intractable, even in some favorable circumstances. We then identify special cases having polynomial time algorithms.

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1996
Reports

Abstract

Official source

Related concepts (32)

Computational complexity theory

In theoretical computer science and mathematics, computational complexity theory focuses on classifying computational problems according to their resource usage, and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm. A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used.

Complexity class

In computational complexity theory, a complexity class is a set of computational problems "of related resource-based complexity". The two most commonly analyzed resources are time and memory. In general, a complexity class is defined in terms of a type of computational problem, a model of computation, and a bounded resource like time or memory. In particular, most complexity classes consist of decision problems that are solvable with a Turing machine, and are differentiated by their time or space (memory) requirements.

Computational complexity

In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) and memory storage requirements. The complexity of a problem is the complexity of the best algorithms that allow solving the problem. The study of the complexity of explicitly given algorithms is called analysis of algorithms, while the study of the complexity of problems is called computational complexity theory.

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