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The paper introduces novel machine learning (data mining) algorithm called Adaptive Local Hyperplane (ALH) and it presents its application in solving regression problems. ALH algorithm has recently shown extremely good results in classification, and it is adopted for solving regression tasks here. It is a local margin maximizing algorithm in the original, weighted, input space blending a Nearest Neighbors...
Stock turning points detection is a very interesting subject arising in numerous financial and economic planning problems. In this paper, evolving neural network model with dynamic time warping piecewise linear representation system for stock turning points detection is presented. The piecewise linear representation method is able to generate numerous stocks turning points from the historic data base,...
A new algorithm, Laplacian minmax discriminant projection (LMMDP), is proposed in this paper for supervised dimensionality reduction. LMMDP aims at learning a linear transformation which is an extension of linear discriminant analysis (LDA). Specifically, we define the within-class scatter and the between-class scatter using similarities which are based on pairwise distances in sample space. After...
One knowledge discovery problem in the rapid response setting is the cost of learning which patterns are indicative of a threat. This typically involves a detailed follow-through, such as review of documents and information by a skilled analyst, or detailed examination of a vehicle at a border crossing point, in deciding which suspicious vehicles require investigation. Assessing various strategies...
The target of this project is to propose and implement incremental system for multi languages linguistic command recognition in multi agent system MASS, based on client-server architecture. Preprocessing is realized with the aid of cepstral coefficients and classification is realized by modified MF Artmap. System allows remote parallel learning of various commands, their consecutive identification...
Rare category detection is the task of identifying examples from rare classes in an unlabeled data set. It is an open challenge in machine learning and plays key roles in real applications such as financial fraud detection, network intrusion detection, astronomy, spam image detection, etc. In this paper, we develop a new graph-based method for rare category detection named GRADE. It makes use of the...
In pattern recognition area, an ensemble approach is one of promising methods to increase the accuracy of classification systems. It is interesting to use the ensemble approach in evolving game strategies because they maintain a population of solutions simultaneously. Simply, an ensemble is formed from a set of strategies evolved in the last generation. There are many decision factors in the ensemble...
Patten recognition techniques are often an important component of intelligent systems to describe, classify and recognise the objects. Object recognition using linear vector quantization neural network which is trained using descriptors such as boundary and regional descriptors is presented in this paper. Out of the various descriptors available, a combination of these descriptors extracted from the...
In recent years, studies of similar music retrieval have been conducted actively. However, because the similarity of music is based on subjective measures, the systems need to be adaptive to user preference. In this paper, we propose an effective method for adaptive similar music retrieval reflecting the user preference by nonlinear feature space transformation based on relevance feedback. The user's...
This paper presents an end-to-end administrative document analysis system. This system uses case-based reasoning in order to process documents from known and unknown classes. For each document, the system retrieves the nearest processing experience in order to analyze and interpret the current document. When a complete analysis is done, this document needs to be added to the document database. This...
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