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.
In various studies, it has been demonstrated that combining the decisions of multiple classifiers can lead to better recognition results. Plurality voting is one of the most widely used combination strategies. In this paper, we both theoretically and experimentally analyze the performance of a plurality voting-based ensemble classifier. Theoretical expressions for system performance are derived as...
Due to the rapid development of motion capture technology, more and more human motion databases appear. In order to effectively and efficiently manage human motion database, human motion classification is necessary. In this paper, we propose an ensemble based human motion classification approach (EHMCA). Specifically, EHMCA first extracts the descriptors from human motion sequences. Then, singular...
In this paper, an efficient feature extraction method named as Constrained Maximum Variance Mapping (CMVM) is developed for dimensionality reduction. The proposed algorithm can be viewed as a linear approximation of multi-manifolds based learning approach, which takes the local geometry and manifold labels into account. After the local scatters have been characterized, the proposed method focuses...
In this paper, we present a biologically inspired method for detecting pedestrians in images. The method is based on a convolutional neural network architecture, which combines feature extraction and classification. The proposed network architecture is much simpler and easier to train than earlier versions. It differs from its predecessors in that the first processing layer consists of a set of pre-defined...
One of the main requirements of biometric systems is the ability of producing very low false acceptation rate, which very often can be achieved only by combining different biometric traits. The literature has shown that the pattern classification approach usually surpasses the classifier combination approach for this task. In this work we take into account the pattern classification approach, but...
Negative Correlation Learning (NCL) has been showing to outperform other ensemble learning approaches in off-line mode. A key point to the success of NCL is that the learning of an ensemble member is influenced by the learning of the others, directly encouraging diversity. However, when applied to on-line learning, NCL presents the problem that part of the diversity has to be built a priori, as the...
We review a form of topology preserving mapping which uses the same underlying structure as the generative topographic mapping (GTM) but organises the projections of the latent points into data space based on the method of harmonic K-means. We show that projections of the Olivetti face database onto this latent space show good performance in terms of identifying all images of a particular individual...
As shown in the bibliography, training an ensemble of networks is an interesting way to improve the performance with respect to a single network. The two key factors to design an ensemble are how to train the individual networks and how to combine them to give a single output. Boosting is a well known methodology to build an ensemble. Some boosting methods use an specific combiner (Boosting Combiner)...
In this paper, we present an artificial neural network (ANN) architecture for segmenting unconstrained handwritten sentences in the English language into single words. Feature extraction is performed on a line of text to feed an ANN that classifies each column image as belonging to a word or gap between words. Thus, a sequence of columns of the same class represents words and inter-word gaps. Through...
In a previous work, we presented an approach for shape-based image retrieval using the curvature scale space (CSS) and self-organizing map (SOM) methods. Here, we examine the robustness of the representation with images under different scales. The shape features of images are represented by CSS images extracted from, for example, a large database and represented by median vectors that constitutes...
This paper describes how recursive nodes with rich dynamics can be explored in a self-organizing artificial network for continuous learning tasks. The purpose of inserting the recursive elements is introducing chaos behavior in a modified self-organizing map (SOM). This new structure is called CSOM. It incorporates some of the main features of SOM, but it also improves the capability of cluster input...
In protein tertiary structure prediction, it is a crucial step to select near-native structures from a large number of candidate structural models. Despite much effort to tackle the problem of protein structure selection, the discerning power of current scoring functions is still unsatisfactory. In this paper, we developed a new clustering-based method for selecting near-native protein structures...
Many results in the literature indicate that the incremental approach to association mining leads to gain regarding the time needed to obtain the rules, but there is no evaluation about their quality, compared to non-incremental algorithms. This paper presents the comparison of usage of two typical algorithms representing each approach: APriori and ZigZag. Execution time clearly shows the advantage...
In this paper, we propose several active learning strategies to train classifiers for phosphorylation site prediction. When combined with support vector machine, we show that active learning with SVM is able to produce classifiers that give comparable or better phosphorylation site prediction performance than conventional SVM techniques and, at the same time, require a significantly less number of...
This paper presents an investigation into prototype-based classifiers. Different methods have been proposed to deal with this problem. There are two main classes of prototype-selection algorithms. The first is merely selective, in which the resulting set of prototypes is formed by well-chosen samples from the training set. The second is known as the creative class of algorithms. This strategy creates...
Face recognition system usually consists of components of feature extraction and pattern classification. However, not all of extracted facial features contribute to the classification phase positively because of the variations of illumination and poses in face images. In this paper, a three-step feature selection algorithm is proposed in which discrete cosine transform (DCT) and genetic algorithms...
In the last two decades the advancement of AI/CI methods in classical board and card games (such as Chess, Checkers, Othello, Go, Poker, Bridge, ...) has been enormous. In nearly all ldquoworld famousrdquo board games humans have been decisively conquered by machines (actually Go remains almost the last redoubt of human supremacy). In the above perspective the natural question is whether there is...
Computer-based automatic human facial expression recognition (FER) is fundamental and indispensable in realizing truly intelligent human-machine interfaces. In this paper, a new FER technique is proposed, which uses lower-frequency 2D DCT coefficients of binarized edge images and constructive one-hidden-layer (OHL) feedforward neural networks (NNs). The 2D DCT is thereby used to compress the binarized...
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.