We analyze a learning method that uses a margin kappa a la Gardner for simple perceptron learning. This method corresponds to the perceptron learning when kappa = 0 and to the Hebbian learning when kappa = infinity. Nevertheless, we found that the generalization ability of the method was superior to that of the perceptron and the Hebbian methods at an early stage of learning. We analyzed the as...

Data mining is the upcoming research area to solve various problems and classification is one of main problem in the field of data mining. In this paper, we use two classification algorithms J48 (which is java implementation of C4. 5 algorithm) and multilayer perceptron alias MLP (which is a modification of the standard linear perceptron) of the Weka interface. It can be used for testing severa...

In this paper we embed m-dimensional Euclidean space in the geometric algebra Clm to extend the operators of incidence in R to operators of incidence in the geometric algebra to generalize the notion of separator to a decision boundary hyperconic in the Clifford algebra of hyperconic sections denoted as Cl(V2). This allows us to extend the concept of a linear perceptron or the spherical percept...

Perceptron predictor is well known as its ability to exploit long history information. A limitation of perceptron predictor is that it cannot effectively predict linearly inseparable correlations. In this paper, we present a revised perceptron predictor with separated Taken/Not-Taken weight tables (SWP). It can treat the taken/not taken correlation separately and therefore predict some linearly...

We evaluate the capacity and performance of a perceptron discriminator operating in a highly sparse regime where classic perceptron results do not apply. The perceptron is constructed to respond to a specified set of q stimuli, with only statistical information provided about other stimuli to which it is not supposed to respond. We compute the probability of both false-positive and false-negati...