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Ensemble pruning is concerned with the reduction of the size of an ensemble prior to its combination. Its purpose is to reduce the space and time complexity of the ensemble and/or to increase the ensemble's accuracy. This paper focuses on instance-based approaches to ensemble pruning, where a different subset of the ensemble may be used for each different unclassified instance. We propose modeling...
This work presents the concept of Continuous Search (CS), which objective is to allow any user to eventually get their constraint solver achieving a top performance on their problems. Continuous Search comes in two modes: the functioning mode solves the user's problem instances using the current heuristics model; the exploration mode reuses these instances to train and improve the heuristics model...
To solve the difficulty of the field of Automatic Entity Relation Extraction, in this paper, a method that used binary classification thinking, meanwhile combined with reasoning rules to extract the field of entity relation is proposed. considering comprehensively the context information of entity, entity type and their combination of characteristics to construct the feature set, which in order to...
In order to improve the accuracy of multi-moving objects detection in surveillant video, this paper presents a new method of detection and segmentation for moving objects based on SVM (support vector machine). To further enhance the accuracy of segmentation using support vector machine, we modify the kernel function based on its nature, and some experiments have been done to compare with other kernel...
A new approach to classification of non-stationary power signals based on adaptive wavelet has been considered. This paper proposes a model for non-stationary power signal disturbance classification using adaptive wavelet networks (AWN). A AWN is a combination of two sub-networks consisting of a wavelet layer and adaptive probabilistic network. The AWN has the capability of automatic adjustment of...
In this paper, we present the problem of appropriate feature selection for constructing a Maximum Entropy (ME) based Named Entity Recognition (NER) system under the multiobjective optimization (MOO) framework. Two conflicting objective functions are simultaneously optimized using the search capability of MOO. These objectives are (i). the dimensionality of features, which is tried to be minimized,...
Instance-based learning algorithms typically suffer influences of dissimilarity functions. The problem is frequently related to the Nearest Neighbor rules of these algorithms. This paper will introduce a new dissimilarity measure, called Heterogeneous Centered Difference Measure, which is tested over many known databases. The results are compared with other distance functions.
A hybrid particle swarm optimization (PSO)-based wavelet neural network (WNN) for Video OCR is presented in this paper. Video OCR is an important task towards enabling automatic content-based retrieval of digital video databases. However, since text is often displayed against a complex background, its detection and extraction is a challenging problem. In this paper, wavelet transformation is done...
Aimming at the ever-present problem of imbalanced data in text classification, the authors study on several forms of imbalanced data, such as text number, class size, subclass and class fold. Some useful conclusions are gotten from a series of correlative experiments: first, when the text of two class is almost the same number, the difference of word number become major factor to affect the accuracy...
The 21st century is the information age. Every university student should take possession of certain information capabilities. It is the basic level of university students. This paper sets forth the contents of the information capabilities, and explores the effective training ways.
To relieve "News Information Overload", classification, summarization and recommendation techniques have been proposed. However, these techniques fail to provide sufficient semantic information about news events. In this paper, considering5W1H (Who, What, Whom, When, Where and How), the full list of elements of a news article, we propose a novel approach to extract event semantic elements...
Planning techniques recorded a significant progress during recent years. However, many planning problems remain still hard even for modern planners. One of the most promising approaches is gathering additional knowledge by using learning techniques. Well known sort of knowledge - macro-operators, formalized like 'normal' planning operators, represent a sequence of primitive planning operators. The...
This paper presents an off-line signature verification system composed of a combination of several different classifiers. Identity authentication is a very important characteristics specially in systems that requires a high degree of security such as in bank transactions. In our experiments, one-class classifier was used to create a signature verification system, consequently only genuine signatures...
This paper presents Perturbed Frequent Itemset based Classification Technique (PERFICT), a novel associative classification approach based on perturbed frequent itemsets. Most of the existing associative classifiers work well on transactional data where each record contains a set of boolean items. They are not very effective in general for relational data that typically contains real valued attributes...
In order to satisfy the needs of human resources management and development, this study took R&D professionals as the research object and proposed an evaluation model for high-tech enterprise human resources based on artificial neural network, then trained and tested the neutral network for personnel evaluation. And the network was improved to be very effective to stimulate the evaluation of human...
With financial globalization, the rapid development of financial derivatives and the complexity of banks management, operational risk measurement and management in commercial bank management is becoming increasingly important. How to effectively predict, control and prevent operational risk in commercial banks have become an important issue. Using BP neural network model to predict the risk has its...
This work studies the use of Particle Swarm Optimization (PSO) as a classification technique. Beyond assessing classification accuracy, it investigates the following questions: does PSO present limitations for high dimensional application domains? Is it less efficient for multi class problems? To answer the questions, an experimental set up was realized that uses three high dimensional data sets....
This paper presents a hybrid intelligent method to design Morphological-Rank-Linear (MRL) perceptrons to solve the Software Development Cost Estimation (SDCE) problem. The proposed method uses a modified genetic algorithm (MGA) to determine the best particular features to improve the MRL perceptron performance, as well as its initial parameters. Furthermore, for each individual of MGA, a gradient...
Face to the actual demand of current society on applicational personnel training, according to the characteristics of the non-computer major students in private colleges and the personnel training objectives, we analyze in depth the problem in the teaching of current private colleges non-computer professional database course. Combined with years of teaching reform and a detailed study of the syllabus,...
In this paper a novel view-invariant movement recognition method is presented. A multi-camera setup is used to capture the movement from different observation angles. Identification of the position of each camera with respect to the subject's body is achieved by a procedure based on morphological operations and the proportions of the human body. Binary body masks from frames of all cameras, consistently...
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