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Machine learning techniques have recently been applied to the problem of lithographic hotspot detection. It is widely believed that they are capable of identifying hotspot patterns unknown to the trained model. The quality of a machine learning method is conventionally measured by the accuracy rates determined from experiments employing random partitioning of benchmark samples into training and testing...
Probabilistic latent semantic analysis is a topic modeling technique to discover the hidden structure in binary and count data. As a mixture model, it performs a probabilistic mixture decomposition on the co-occurrence matrix, which produces two matrices assigned with probabilistic explanations. However, the factorized matrices may be rather smooth, which means we may obtain global feature and topic...
This paper presents an optimizing methodology for implementing a multi-layer perceptron (MLP) neural network in a Field Programmable Gate Array (FPGA) device. In order to obtain an efficient implementation, a compromise of time and area is needed. Starting from simulation in the learning phase with fixed point operators, we have developed a methodology which allows the automatic generation of a VHDL...
The code matrix enables to convert a multi class problem into an ensemble of binary classifiers. We suggest a new un-weighted framework for iteratively extending the code matrix which based on confusion matrix. The confusion matrix holds important information which is exploited by the suggested framework. Evaluating the confusion matrix at each iteration enables to make a decision regarding the next...
In this paper, we present a novel approach, namely directional multi-mode principal component analysis, which efficiently avoids the small sample size problem and preserves the spatial information embed in among pixels of image, by encoding the input high-dimensional image as a tensor. In the proposed scheme, the mode-k matrix of the image is re-sampled and re-arranged to form a mode-k directional...
This article proposes a general extension of the Error Correcting Output Codes (ECOC) framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. Validation on UCI database and two real machine vision applications show that the online problem-dependent ECOC proposal provides a feasible and robust way for...
Error Correcting Output Codes (ECOC) method solves multiclass learning problems by combining the outputs of several binary classifiers according to an error correcting output code matrix. Traditionally, the minimum Hamming distance is adopted as the classification criterion to "vote" among multiple hypotheses, and the focus is given to the choice of error correcting output code matrix. In...
In this paper, we present Dynamic Terminology Enhancement Method (DTEM) to support enrichment and extensibility in a biosignal integration system called ROISES (Research Oriented Integration System for ECG signals), which integrates diversely encoded ECG signals and the corresponding annotation and metadata. The diverse datasources are homogenized through the mapping of their schemas to an ECG specialized...
In this paper, we propose A two-stage learning scheme for neural networks by integrating Gas into Structure identification In the first stage, which is also called structure identification stage, the selection of network structure and initial parameters is carried out by float genetic algorithm instead of human ln the second stage which is called parameter identification stage the conventional optimization...
In recent years, many studies have focused on improving the accuracy of prediction of trans-membrane segments, and many significant results have been achieved. In spite of these considerable results, the existing methods lack the ability to explain the process of how a learning result is reached and why a prediction decision is made. The explanation of the decision process is important for acceptance...
Classification of network traffic is basic and essential for many network researches and managements. However, classification of network traffic using port-based and simple payload-based methods is diminished with the rapid development of peer-to-peer (P2P) application using dynamic port, disguising techniques and encryption to avoid detection. An alternative method based on statistics and machine...
This paper presents an approach based on Projection Pursuit and fuzzy rule extraction combining new hybrid method of classification system. This method is the first to use projection pursuit technology to deal with training set of sample dimensionality reduction and in accordance with the sample classification. According to the results of the classification and the best value projection, using trapezoid...
Web page prefetching has been used efficiently to reduce the access latency problem of the Internet, its success mainly relies on the accuracy of Web page prediction. As powerful sequential learning models, conditional random fields (CRFs) have been used successfully to improve the Web page prediction accuracy when the total number of unique Web pages is small. However, because the training complexity...
This paper presents a new hybrid learning algorithm based on cooperative coevolutionary algorithm (Co-CEA) for designing the radial basis function neural network (RBFNN) classifiers with an inductive feature selection. The hidden layer design and the feature selection correspond to two subpopulations. Collaborations among the two subpopulations are formed to obtain complete solutions. Experimental...
The accurate recognition of translation initiation sites (TISs) is an important stage in genome annotation. Due to the complicated nature of the genetic information and our incomplete understanding of it, TIS prediction remains a challenging undertaking. Many computational approaches have been proposed in the literature, some of which have yielded quite impressive performance. However, most of them...
We propose a supervised approach to word sense disambiguation based on neural networks combined with evolutionary algorithms. Large tagged datasets for every sense of a polysemous word are considered, and used to evolve an optimized neural network that correctly disambiguates the sense of the given word considering the context in which it occurs. The viability of the approach has been demonstrated...
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