The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Recent research on eco-driving has paid more attention on human driving behavior and vehicle performance, but neglects the impacts imposed by dynamic, discrete and distributive traffic, which may result in partial and limited eco-driving suggestions. This paper presents a multi-factor integration based eco-driving optimization method for vehicles with same driving characteristics. It constructs driving...
The Gaussian mixture model (GMM) has been widely used in pattern recognition problems for clustering and probability density estimation. Given the number of mixture components (model order), the parameters of GMM can be estimated by the EM algorithm. The model order selection, however, remains an open problem. For classification purpose, we propose a discriminative model selection method to optimize...
Most of constraint handling papers have focused on the selection of individuals by trade-off the feasible and infeasible regions. This paper studies the effect of two kinds of reproduction in constraint multiobjective optimization. It compares a probabilistic model-based multiobjective evolutionary algorithm to a genetic algorithm. They all use a min-max selection strategy as the main frame structure...
This paper presents a text query-based method for keyword spotting from online Chinese handwritten documents. The similarity between a text word and handwriting is obtained by combining the character similiarity scores given by a character classifier. To overcome the ambiguity of character segmentation, multiple candidates of character patterns are generated by over-segmentation, and sequences of...
One major task of online writeprint identification is to select the key features for representing the writeprint and facilitating the classifier built by using only the selected feature subset. In this study, we develop a hybrid genetic algorithm: RelieF Fed Genetic Algorithm (RFGA) which incorporates feature weight information produced by using RelieF as the heuristic to identity the key features...
Different biological labs conduct similar experiments on same diseases. It is highly desirable to have a better model based on more experimental results than that on a single result. In this paper, we propose a method for integrating microarray data from multiple sources for building classification models. To test the method, we use three real world microarray data sets from different labs with different...
Latent semantic indexing (LSI) is an effective technique for feature extraction in text mining, and supervised LSI (SLSI) algorithms have been proposed to exploit the class labels of training data. In this paper, we propose an iterative SLSI framework based on class selection. We show that a previous iterative SLSI algorithm is an instance of the framework. We also propose a method under our framework,...
Low information transfer rate (ITR) is one of main problems that a brain-computer interface (BCI) faces. One method to increase ITR is to extend two-class mental tasks to multiple tasks. Accordingly an efficient method for feature extraction is needed to ensure good classification performance. This paper generalizes well-known common spatial pattern (CSP) algorithm from two task conditions to multi-task...
Data classification has been studied widely in the fields of Artificial Intelligence, Machine Learning, Data Mining and Pattern Recognition. Up to the present, the development of classification has made great achievements, and many kinds of classified technology and theory will continue to emerge. This paper discusses a great deal of classification algorithms based on the Artificial Neural Networks,...
The classification performance of nearest prototype classifiers largely relies on the prototype learning algorithms, such as the learning vector quantization (LVQ) and the minimum classification error (MCE). This paper proposes a new prototype learning algorithm based on the minimization of a conditional log-likelihood loss (CLL), called log-likelihood of margin (LOGM). A regularization term is added...
In this paper, we present a robust system to accurately detect and localize texts in natural scene images. For text detection, a region-based method utilizing multiple features and cascade AdaBoost classifier is adopted. For text localization, a window grouping method integrating text line competition analysis is used to generate text lines. Then within each text line, local binarization is used to...
This paper presents a new method to improve the classification performance for remote-sensing applications based on swarm intelligence. Traditional statistical classifiers have limitations in solving complex classification problems because of their strict assumptions. For example, data correlation between bands of remote-sensing imagery has caused problems in generating satisfactory classification...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.