We show that indeed, noisy perceptrons are PAC learnable with a hypothesis that is a perceptron.
We consider the problem where labels are subjected to random classification noise. The problem was known to be PAC learnable via a hypothesis that consists of a ...
Hence, the perceptron algorithm does not have a polynomial running time. This paper focuses on the noisy version of the per- ceptron problem with random classi ...
We consider the problem where labels are subjected to random classification noise. The problem was known to be PAC learnable via a hypothesis that consists of a ...
Bibliographic details on Learning Noisy Perceptrons by a Perceptron in Polynomial Time.
People also ask
What is one advantage of using multiple Perceptrons in a neural network instead of just training a single perceptron?
How do you train a single Perceptron?
Learning noisy perceptrons by a perceptron in polynomial time · E. Cohen. Computer Science, Mathematics. Proceedings 38th Annual Symposium on Foundations… 1997.
Abstract. In this paper we consider the problem of learning a linear threshold function (a half- space in n dimensions, also called a \perceptron").
Abstract. In this paper we consider the problem of learning a linear threshold function (a half- space in n dimensions, also called a "perceptron").
Missing: Perceptrons | Show results with:Perceptrons
The authors consider the problem of learning a linear threshold function (a halfspace in n dimensions, also called a "perceptron"). Methods for solving this ...
Our proposed approach relies on the combination of the technique of random or deterministic projections with a classification noise tolerant perceptron learning ...