In this paper, we propose to apply the Information Bottleneck (IB) approach to a sub-class of Statistical Relational Learning (SRL) languages. Learning parameters in SRL dealing with domains that involve hidden variables requires the use of techniques for learning from incomplete data such as the expectation maximization (EM) algorithm. Recently, IB was shown to overcome well known problems of ...

AbetractRather than iteratively manually examining a variety of pre-specified architectures, a constructive learning algorithm dynamically creates a problem-specific neural network architecture. Here we present an revised version of our parallel constructive neural network learning algorithm which constructs such an architecture. The three steps of searching for points on separating hyperplanes...

This paper considers knowledge discovery by sort regular patterns, which are strings over sort letters representing nite sets of basic letters. We devise a learning algorithm for the class based on the minimal multiple generalization technique, and evaluate the method by experiments on biosequences from GenBank database. The experiments show that relatively a simple sort pattern can represent a...

Support vector machine is a machine learning algorithm with good performance, its parameters have an important influence on accuracy of classification, and parameters selection is becoming one of the main research areas of machine learning. This paper adopt support vector machine to recognize the characters of license plate. But in order to get good parameters of support vector machine, this pa...

A particularly interesting instance of supervised learning with kernels is when each training example is associated with two objects, as in pairwise classification (Brunner et al., 2012), and in supervised learning of preference relations (Herbrich et al., 1998). In these cases, one may want to embed additional prior knowledge into the optimization problem associated with the training of the le...

Our objective is to train p-norm Multiple Kernel Learning (MKL) and, more generally, linear MKL regularised by the Bregman divergence, using the Sequential Minimal Optimization (SMO) algorithm. The SMO algorithm is simple, easy to implement and adapt, and efficiently scales to large problems. As a result, it has gained widespread acceptance and SVMs are routinely trained using SMO in diverse re...

Abstract In this paper, a novel technique based on fuzzy method is presented for chaotic nonlinear time series prediction. Fuzzy approach with the gradient learning algorithm and methods constitutes the main components of this method. This learning process in this method is similar to conventional gradient descent learning process, except that the input patterns and parameters are stored in mem...

Appropriate bias is widely viewed as the key to efficient learning and generalization. I present a new algorithm, the Incremental Delta-Bar-Delta (IDBD) algorithm, for the learning of appropriate biases based on previous learning experience. The IDBD algorithm is developed for the case of a simple, linear learning system—the LMS or delta rule with a separate learning-rate parameter for each inp...

The present work proposes a system for assistance of Autistic children by analysis of eye movements. Autism is a disease characterized by abnormal eye movements and an inability to follow a pattern of object movement in different directions. Eye movement data is recorded from normal individuals over a period of five days using an Electrooculogram signal acquisition system developed in the labor...

This paper presents a machine learning approach for identification of Bengali multiword expressions (MWE) which are bigram nominal compounds. Our proposed approach has two steps: (1) candidate extraction using chunk information and various heuristic rules and (2) training the machine learning algorithm called Random Forest to classify the candidates into two groups: bigram nominal compound MWE ...