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In this paper, according to the difference between the attack categories, we adjust the 41-dimensional input features of the neural-network-based multiple classifiers intrusion detection system. After repeated experiment, we find that the every adjusted sub-classifier is better in convergence precision, shorter in training time than the 41-features sub-classifier, moreover, the whole intrusion detection...
In this paper a new optimization algorithm based on Chaos Optimization algorithm(COA) combined with traditional Baum Welch (BW) method is presented for training Hidden Markov Model (HMM) for Continues speech recognition. The BW algorithm easily trapped in local optimum, which might deteriorate the speech recognition rate, while an important character of COA is global search. so we can get a globally...
Sample database was established and the mapping relationship between span,rise-span ratios and type of shell with minimum weight was simulated by using BP neural network method. The selected typical samples were chosen from hundreds of sectional optimized results based on sequential two-level algorithm from five typical types of reticulated domes. This paper provides a simple lectotype optimization...
Convertible bonds (CB) contain many kinds of embedded options and the complexity of their interaction makes hedging exposures of CBs challengeable. In order to tackle the issue, this paper introduced support vector machine (SVM) approach to overcome the shortcomings of traditional pricing methods and enhance hedging efficiency. By feature selection, kernel function determination and parameter optimization,...
In this paper, we propose a cooperative learning algorithm for Multi-category classification which is decomposed into two sub-optimization problems by using the support vector machine technique. The proposed cooperative learning algorithm consists of two single learning algorithms and each sub-optimization problem is solved by one of them. Unlike the cooperative neural network, the proposed cooperative...
New pattern recognition method is considered that is based on ensembles of ”syndromes”. The developed method that is referred to as Multi-model statistically weighted syndromes (MSWS) is further development of earlier Statistically Weighted Syndromes (SWS) method. ”Syndromes” are subregions in space of prognostic features where content of objects from one of the classes differs significantly from...
The standard 2-norm support vector machine (SVM for short) is known for its good performance in classification and regression problems. In this paper, the 1-norm support vector machine is considered and a novel smoothing function method for Support Vector Classification(SVC) and Regression (SVR) are proposed in an attempt to overcome some drawbacks of the former methods which are complex, subtle,...
Object category detection with large appearance variation is a fundamental problem in computer vision. The appearance of object categories can change due to intra-class variability, viewpoint, and illumination. For object categories with large appearance change a sub-categorization based approach is necessary. This paper proposes a sub-category optimization approach that automatically divides an object...
Unlike most previous manifold-based data classification algorithms assume that all the data points are on a single manifold, we expect that data from different classes may reside on different manifolds of possible different dimensions. Therefore, better classification accuracy would be achieved by modeling the data by multiple manifolds each corresponding to a class. To this end, a general framework...
Appropriate feature selection is a very crucial issue in any machine learning framework, specially in Maximum Entropy (ME). In this paper, the selection of appropriate features for constructing a ME based Named Entity Recognition (NER) system is posed as a multiobjective optimization (MOO) problem. Two classification quality measures, namely recall and precision are simultaneously optimized using...
In this paper, an extended locality preserving discriminant analysis (ELPDA) method is proposed. To address the disadvantages of original locality preserving discriminant analysis (LPDA), a new locality preserving between-class scatter, which is characterized by samples and the corresponding k out-class nearest neighbors, is defined. Moreover, the small sample size problem is also avoided by solving...
Face sketch synthesis with a photo is challenging due to that the psychological mechanism of sketch generation is difficult to be expressed precisely by rules. Current learning-based sketch synthesis methods concentrate on learning the rules by optimizing cost functions with low-level image features. In this paper, a new face sketch synthesis method is presented, which is inspired by recent advances...
In the biometric verification, authentication is given when a distance of biometric signatures between enrollment and test phases is less than an acceptance threshold, and the performance is usually evaluated by a so-called Receiver Operating Characteristics (ROC) curve expressing a trade off between False Rejection Rate (FRR) and False Acceptance Rate (FAR). On the other hand, it is also well known...
Instead of solving complex pattern recognition problems using a single complicated classifier, it is often beneficial to leverage our prior knowledge and decompose the problem into parts. These may be tackled using specific feature subsets and simpler classifiers resulting in a hierarchical system. In this paper, we propose an efficient and scalable approach for cost-sensitive optimization of a general...
Support vector machine (SVM) algorithm has shown a good learning ability and generalization ability in classification, regression and forecasting. This paper mainly analyzes the the performance of support vector machine algorithm in the classification problem, including the algorithm in the kernel function selection, parameter optimization, and integration of other algorithms and to deal with multi-classification...
Genetic programming approaches have previously been employed in the literature to evolve heuristics for various combinatorial optimisation problems. This paper presents a hyper-heuristic genetic programming methodology to evolve more sophisticated one dimensional bin packing heuristics than have been evolved previously. The heuristics have access to a memory, which allows them to make decisions with...
Real-world design optimization problems are typically computationally-expensive and to address this various model-assisted evolutionary frameworks have been proposed. However, often such problems are also high-dimensional and in such settings models tend to have poor accuracy and thus degrade the optimization search. To address this we propose two complementary dimensionality-reduction frameworks...
Recently, many papers have proposed automatic techniques for · extraction of knowledge from numerical data. · minimization of the number of rules. But few works have been developed for design of experiments and datum plane covering. Most of optimization methods make the assumption that datum plane is sufficiently covered. If this assumption no longer holds, we will see that these methods may not work,...
This paper proposes a novel semantics-based consumer photo adaptation scheme for users of small-display mobile devices. The main contributions of the proposed scheme are: (1) seamless integration of mobile user supplied semantic information with low level image features to identify se-mantically important regions-of-interest (ROI), and (2) perceptually optimized adaptation for photo display on mobile...
This paper presents a novel method of generating new probability distributions tailored to specific problem classes for use in optimisation mutation operators. A range of tailored operators with varying behaviours are created using the proposed technique and the evolved multi-modal polynomial distributions are found to match the performance of a tuned Gaussian distribution when applied to a mutation...
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