In this study, a technique was developed to compensate for nonlinear distortion through cancelling subcarrier-to-subcarrier intermixing interference (SSII) in an electroabsorption modulator (EAM)-based orthogonal frequency-division multiplexing (OFDM) transmission system. The nonlinear distortion to be compensated for is induced by both EAM nonlinearity and fiber dispersion. Because an OFDM sig...

We present a new perspective for investigating the Probably Approximate Correct (PAC) learnability of classes of concepts. We focus on special sets of points for characterizing the concepts within their class. This gives rise to a general notion of boundary of a concept, which holds even in discrete spaces, and to a special probability measuring technique. This technique is applied (i) to narro...

Protein conformation problem, one of the hard and important problems, is to identify conformation rules which transform sequences to their tertiary structures, called conformations. Our aim of this work is to give a concrete theoretical foundation for graph-theoretic approach for the protein conformation problem in the framework of a probabilistic learning model. We propose the conformation pro...

A new proof of a result due to Vapnik is given. Its implications for the theory of PAC learnability are discussed, with particular reference to the learnability of functions taking values in a countable set. An application to the theory of artificial neural networks is then given.

Since it is difficult to know the correct bias for an inductive learning problem a priori, the ability to detect a bad bias can be valuable. One method is to take advantage of an algorithm that makes strong performance guarantees when the bias is correct, then verify that the algorithm performs as promised. This paper develops this idea within the context of the Valiant framework. In the basic ...

In order to be useful in practice, machine learning algorithms must tolerate noisy inputs. In this paper we compare and contrast the effects of four different types of noise on learning in Valiant’s PAC (probably approximately correct), or distribution-free, model of learning [ 111. Two previously studied models, malicious noise [ 121 and random classification noise [ 11, represent the extremes...