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In this paper, we propose several active learning strategies to train classifiers for phosphorylation site prediction. When combined with support vector machine, we show that active learning with SVM is able to produce classifiers that give comparable or better phosphorylation site prediction performance than conventional SVM techniques and, at the same time, require a significantly less number of...
Self organizing map (SOM) is a kind of artificial neural network with a competitive and unsupervised learning. This technique is commonly used to dataset clustering tasks and can be useful in patterns recognition problems. This paper presents an artificial neural network application to signals language recognition problem, where the image representation is given by bit signatures. The recognition...
Penalized likelihood regression is a concept whereby the log-likelihood of the observations is combined with a term measuring the smoothness of the fit, and the resulting expression is then optimized. This concept vies for achieving a compromise between goodness of fit (as typified by the likelihood function) and smoothness of the data. Penalized likelihood regression, which has been developed in...
This paper presents an on-line, continuously learning mechanism for sequence data. The proposed approach is based on SOINN-DTW method (Okada and Hasegawa, 2007), which is designed for learning of sequence data. It is based on self-organizing incremental neural network (SOINN) and dynamic time warping (DTW). Using SOINNpsilas function represents the topological structure of online input data, the output...
Dynamic multi-objective optimization (DMO) is one of the most challenging class of optimization problems where the objective functions change over time and the optimization algorithm is required to identify the corresponding Pareto optimal solutions with minimal time lag. DMO has received very little attention in the past and none of the existing multi-objective algorithms perform satisfactorily on...
Anomaly detection provides an early warning of unusual behavior in units in a fleet operating in a dynamic environment by learning system characteristics from normal operational data and flagging any unanticipated or unseen patterns. For a complex system such as an aircraft engine, normal operation might consist of multiple modes in a high dimensional space. Therefore, anomaly detection approaches...
Effective use of support vector machines (SVMs) in classification necessitates the appropriate choice of a kernel. Designing problem specific kernels involves the definition of a similarity measure, with the condition that kernels are positive semi-definite (PSD). An alternative approach which places no such restrictions on the similarity measure is to construct a set of inputs and let each example...
Meta-Learning has been used to predict the performance of learning algorithms based on descriptive features of the learning problems. Each training example in this context, i.e. each meta-example, stores the features of a given problem and information about the empirical performance obtained by the candidate algorithms on that problem. The process of constructing a set of meta-examples may be expensive,...
The Self-Organizing map (SOM) proposed by T. Kohonen is a method to produce a low-dimensional representation from high-dimensional input data automatically, where output units are restrictedly placed on grid points. We propose real-number SOM (RSOM), where output units are freely placed on the real-number coordinates plane and visualized as a Voronoi diagram. RSOM is a natural extension of the conventional...
This paper presents a fuzzy ARTMAP (FAM) based modular architecture for multi-class pattern recognition known as modular adaptive resonance theory map (MARTMAP). The prediction of class membership is made collectively by combining outputs from multiple novelty detectors. Distance-based familiarity discrimination is introduced to improve the robustness of MARTMAP in the presence of noise. The effectiveness...
In this paper, a new line symmetry based classifier (LSC) is proposed to deal with pattern classification problems. In order to measure total amount of line symmetry of a particular point in a class, a new definition of line symmetry based distance is also proposed in this paper. The proposed line symmetry based classifier (LSC) utilizes this new definition of line symmetry distance for classifying...
The cerebral cortex uses a large number of top-down connections, but the roles of the top-down connections remain unclear. Through end-to-end (sensor-to-motor) multilayered networks that use three types of connections (bottom-up, lateral, and top-down), the new topographic class grouping (TCG) mechanism shown in this paper explains how the top-down connections influence (1) the type of feature detectors...
The generation of weights is an alternative method of loading a set of weights into an artificial neural network. It is a process that transforms a trained base net by multiplying its weights by symmetric matrices [1]. These weights are then assigned to a derived net. The derived nets map symmetrically related functions. At present, the process is limited because it cannot be applied to one-to-many...
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