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A new linear dimensionality reduction (LDR) technique for pattern classification and machine learning is presented, which, though linear, aims at maximizing the Chernoff distance in the transformed space. The corresponding two-class criterion, which is maximized via a gradient-based algorithm, is presented and initialization procedures are also discussed.(More)
BACKGROUND Processing cDNA microarray images is a crucial step in gene expression analysis, since any errors in early stages affect subsequent steps, leading to possibly erroneous biological conclusions. When processing the underlying images, accurately separating the sub-grids and spots is extremely important for subsequent steps that include segmentation,(More)
In this paper, we formally present a novel estimation method, referred to as the Stochastic Learning Weak Estimator (SLWE), which yields the estimate of the parameters of a binomial distribution, where the convergence of the estimate is weak, i.e. with regard to the rst and second moments. The estimation is based on the principles of stochastic learning.(More)
Linear dimensionality reduction (LDR) techniques are quite important in pattern recognition due to their linear time complexity and simplicity. In this paper, we present a novel LDR technique which, though linear, aims to maximize the Chernoff distance in the transformed space; thus, augmenting the class separability in such a space. We present the(More)
Linear Dimensionality Reduction (LDR) techniques have been increasingly important in Pattern Recognition (PR) due to the fact that they permit a relatively simple mapping of the problem onto a lower-dimensional subspace, leading to simple and computationally efficient classification strategies. Although the field has been well developed for the two-class(More)
This correspondence shows that learning automata techniques, which have been useful in developing weak estimators, can be applied to data compression applications in which the data distributions are nonstationary. The adaptive coding scheme utilizes stochastic learning-based weak estimation techniques to adaptively update the probabilities of the source(More)
Protein-protein interactions (PPI) are important in most biological processes and their study is crucial in many applications. Identification of types of protein complexes is a particular problem that has drawn the attention of the research community in the past few years. We focus on obligate and non-obligate complexes, their prediction and analysis. We(More)
The protein family classification problem, which consists of determining the family memberships of given unknown protein sequences, is very important for a biologist for many practical reasons, such as drug discovery, prediction of molecular functions and medical diagnosis. Neural networks and bayesian methods have performed well on the protein(More)