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Spam messages pose a major threat to the usability of electronic mail. Spam wastes time and money for network users and administrators, consumes network bandwidth and storage space, and slows down email servers. In addition, it provides a medium to distribute harmful code and/or offensive content. In this paper, we investigate the application of abductive learning in filtering out spam messages. We...
Structured diagrams are very prevalent in many document types. Most people who need to create such diagrams use structured graphics editors such as Microsoft Visio. Structured graphics editors are extremely powerful and expressive but they can be cumbersome to use. We have shown through extensive timing experiments that structured diagrams drawn by hand will take only about 10% of the time it takes...
With the computer accurate estimation of electronic parts defect detection in quality control play an important role in the manufacturing industry. In order to realization electronic parts product appearance quality detection control, one kind of processor based on the intelligent knowledge automatic extraction and system integration modeling was presented This paper proposes a method using an adaptive...
Salient objects detection in time sequenced images has a very important role in many applications such as surveillance systems, tracking and recognition systems, scene analysis and so on. This paper presents a novel approach for salient objects detection in time sequenced images. The approach in this paper is based on a visual saliency model which is proposed for analysis in time sequenced images...
In this work we use support vector machine to predict polyadenylation sites (Poly (A) sites) in human DNA and mRNA sequences by analyzing features around them. Two models are created. The first model identifies the possible location of the Poly (A) site effectively. The second model distinguishes between true and false Poly (A) sites, hence effectively detect the region where Poly (A) sites and transcription...
Considering unstable characteristics of vibration signals with mechanical failure, the Wigner-Ville distributions (WVD) of vibration acceleration signals, which were acquired from the cylinder head in eight different states of valve train, were calculated and displayed in grey images. Non-negative matrix factorization (NMF) as a useful decomposition for multivariate data and neural network ensembles...
The primary goals of any frequent pattern mining algorithm are to reduce the number of candidates generated and tested as well as number of scan of database required and scan the database as small as possible. In this paper, we focus on reducing database scans and avoiding candidate generation. To achieve this objective a graph theoretic algorithm has been developed. The whole database is compressed...
In this paper, a novel approach based on Gaussian Chirplet Atoms is presented to automatically recognise radar emitter signals. Firstly, based on the over-completed dictionary of Gaussian Chirplet atoms, the improved matching pursuit (MP) algorithm is applied to extract the features of the time-frequency atoms from the typical radar emitter signals, and FFT is introduced to effectively reduce the...
Conventional ensemble learning algorithms based on ambiguity decomposition and negative correlation learning theory are carried out on the basis of empirical risk minimization principle. When SVM is used as the component learner, the generalization ability of ensemble learning system may not be improved. In this paper, based on the estimation of the generalization performance of SVM and negative correlation...
Backpropagation (BP) learning algorithm is the most widely supervised learning technique which is extensively applied in the training of multi-layer feed-forward neural networks. Many modifications have been proposed to improve the performance of BP, and BP with Magnified Gradient Function (MGFPROP) is one of the fast learning algorithms which improve both the convergence rate and the global convergence...
The paper proposes a real-time tracking algorithm for a moving object with mobile robot based on vision using adaptive color matching and Kalman filter. The adaptive color matching can limit the region containing moving object on vision image plane. It can adjust color matching threshold to reduce the influence of lighting variations in the scene. Kalman filter is used as our prediction module to...
Multi-class approaches for SVM (Support Vector Machines) is a very important issue for solving many practical problems (such as OCR and face recognition), since SVM was originally designed for binary class classification. Lots of methods based on traditional binary SVM have been proposed, each with its advantages and disadvantages. Among them, one-versus-one, one-versus-all, directed acyclic graph...
We describe in this paper a comparative study of fuzzy inference systems as methods of integration in modular neural networks (MNNpsilas) for multimodal biometry. These methods of integration are based on type-1 and type-2 fuzzy logic. Also, the fuzzy systems are optimized with simple genetic algorithms. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic...
This paper proposes an improved method to modeling the dynamic process of basic oxygen furnace (BOF) and the main idea is simplification. Aiming at the problem that it is usually difficult to build a precise endpoint dynamic model because of the high dimensional input variables which affect the final results - carbon content and temperature, this paper builds endpoint carbon content prediction model...
Traditional recognition methods which mainly match object images with their skeleton couldnpsilat resolve well complex objectspsila recognition problems. So in the paper, with an introduction and improvement of moment invariants, a new image recognition method is proposed with the combination of skeleton and moment invariants. Firstly, the paper analyses the thoughts of method. Then, the concept of...
This paper presents a modified version of U-tree (A.K. McCallum, 1996), a memory-based reinforcement learning (RL) algorithm that uses selective perception and short-term memory to handle partially observable Markovian decision processes (POMDP). Conventional RL algorithms rely on a set of pre-defined states to model the environment, even though it can learn the state transitions from experience....
This study theoretically analyzes and numerically explores the relationship between the physiological data and three diabetic microvascular complications: diabetic retinopathy, diabetic nephropathy, and diabetic neuropathy (foot problem). Method: The analysis results of 8,736 diabetic patients in northern Taiwan by using two data mining models: C5.0 and neural network were presented and compared....
This paper uses a combination of K-Iterations Fast Learning Artificial Neural Network (KFLANN) and Gabor filters to create a Gabor signature classifier. Gabor filters are known to be useful in modeling responses of the receptive fields and the properties of simple cells in the visual cortex. The responses produced by Gabor filters produce good quantifiers of the visual content in any given image....
We propose an approach to improve the accuracy of estimating feature points of human body on a vision-based motion capture system (MCS) by using the Variable-Density Self-Organizing Map (VDSOM). The VDSOM is a kind of Self-Organizing Map (SOM) and has an ability to learn training samples incrementally. We let VDSOM learn 3-D feature points of human body when the MCS succeeded in estimating them correctly...
Reduction of feature dimensionality is of considerable importance in machine learning. The generalization performance of classification system improves when correlated and redundant features are removed. In order to reduce the dimensionality of pattern representation, A new feature election method for support vector machine is proposed. Based on pattern similarity measurement in kernel space, lass...
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