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In this paper, a multi-agent system is introduced to parallelize the Flexible Beta Basis Function Neural Network (FBBFNT)’ training as a response to the time cost challenge. Different agents are formed; a Structure Agent is designed for the FBBFNT structure optimization and a variable set of Parameter Agents is used for the FBBFNT parameter optimization. The main objectives of the FBBFNT learning...
Online Feature Selection (OFS) is an important technique in pattern recognition and machine learning. Our challenge is how to enhance the classification performance in real contexts where the large-scale training data arrive sequentially with a big number of features. The major problem is how to choose the best accurate and efficient state-of-the art OFS method that can select the relevant features...
Feature selection is an important technique in machine learning and pattern classification. Most existing studies of feature selection are using the batch learning methods. Such methods are not appropriate for real-world applications especially when data arrive sequentially. Recently, this problem is addressed by some feature selection techniques using online learning. Despite the advantages in efficiency...
The major issue of researchers in ANN field is the optimization of the training process including time cost and NN structure. In response to the long training time, Multi-Agent architecture of feed forward Flexible Neural Tree model (MAFNT) is introduced for parallelizing the NN training. Moreover, looking for the best topology of NN, for a given problem, accounts for the large feasible solutions...
Page segmentation and classification is very important in document layout analysis system before it is presented to an OCR system or for any other subsequent processing steps. In this paper, we propose an accurate and suitably designed system for complex documents segmentation. This system is based on steerable pyramid transform. The features extracted from pyramid sub-bands serve to locate and classify...
We present in this paper a framework for audio concept identification based on audio stream analysis and binary classifiers encapsulation. The system consists of three stages. The first stage is called the pre-processing level audio, where audio stream is segmented and silence segments are detected. In the second stage, speech, music and environmental sounds are automatically divided and further classified...
Audio classification has been becoming very important in the field of multimedia researches dealing with audio processing and pattern recognition. Although major of them are focusing in “how”? Audio classifications should be semantic; the majority of them have neglected the importance of preprocessing step of environmental sound recognition, or using simply very classic sound classifiers. The originality...
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