Artificial Neural Networks(ANN) has been phenomenally successful on various pattern recognition tasks. However, the design of neural networks rely heavily on the experience and intuitions of individual developers. In this article, the author introduces a mathematical structure called MLP algebra on the set of all Multilayer Perceptron Neural Networks(MLP), which can serve as a guiding principle...

Semantic annotation influence on coreference detection using perceptron approach The ConLL-2011/2012 evaluation campaign was dedicated to coreference detection systems. This paper presents the coreference resolution system Poly-co submitted to the closed track of the CoNLL-2011 Shared Task and evaluate is potential of evolution when it includes a semantic feature. Our system integrates a multil...

In theory, the Winnow multiplicative update has certain advantages over the Perceptron additive update when there are many irrelevant attributes. Recently, there has been much effort on enhancing the Perceptron algorithm by using regularization, leading to a class of linear classification methods called support vector machines. Similarly, it is also possible to apply the regularization idea to ...

Abs t r ac t . A novel approach to estimate generalisation errors of the simple perceptron of the worst case is introduced. It is well known that the generaiisation error of the simple perceptron is of the form d# with an unknown constant d which depends only on the dimension of inputs, where t is the number of learned examples. Based upon extreme value theory in statistics we obtain an exact f...

This paper describes an incremental parsing approach where parameters are estimated using a variant of the perceptron algorithm. A beam-search algorithm is used during both training and decoding phases of the method. The perceptron approach was implemented with the same feature set as that of an existing generative model (Roark, 2001a), and experimental results show that it gives competitive pe...

We present a statistical method that exactly learns the class of constant depth μ-perceptron networks with weights taken from {−1, 0 + 1} and arbitrary thresholds when the distribution that generates the input examples is member of the family of product distributions. These networks (also known as nonoverlapping perceptron networks or read-once formulas over a weighted threshold basis) are loop...

In this work we present a variational formulation for a multilayer perceptron neural network. With this formulation any learning task for the neural network is defined in terms of finding a function that is an extremal for some functional. Thus the multilayer perceptron provides a direct method for solving general variational problems. The application of this numerical method is investigated th...

Abstract. The statistical properties of the likelihood ratio test statistic (LRTS) for mixture-of-expert models are addressed in this paper. This question is essential when estimating the number of experts in the model. Our purpose is to extend the existing results for mixtures (Liu and Shao, 2003) and mixtures of multilayer perceptrons (Olteanu and Rynkiewicz, 2008). In this paper we study a s...

The nearest neighbor and the perceptron algorithms are intuitively motivated by the aims to exploit the “cluster” and “linear separation” structure of the data to be classified, respectively. We develop a new online perceptron-like algorithm, Pounce, to exploit both types of structure. We refine the usual margin-based analysis of a perceptron-like algorithm to now additionally reflect the clust...