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The ultimate goal in a multiple classifier system (MCS) is to obtain a global and more accurate model through the combination of several base learners. Among the popular combining rules, averaging has been emphasized as a well qualified option. The averaging rule can be applied with equal (simple averaging) or non-equal (weighted averaging) weights vector for the linear combination. When the formed...
Neighbor discovery is vital for initialing and maintaining self-organized ad hoc networks. In order to exchange advertise messages and learn about each other presence, nodes have to aim their antennas toward each other when they are only utilized by directional antennas. Since ad hoc networks nodes have no former information about each other, neighbor discovery in directional ad hoc networks becomes...
Parallel corpora are essential for training statistical machine translation models. Since parallel sentence-aligned corpora are usually noisy due to inexact automatic methods when generated from parallel or comparable documents, we need to clean parallel corpora. In this paper, new features are introduced to assess the correctness of a sentence pair. Also, the impact of new features in combination...
The problem of spam detection is a crucial task in the web information retrieval systems. The dynamic nature of information resources as well as the continuous changes in the information demands of the users makes the task of web spam detection a challenging topic. So far many different methods from researchers with different backgrounds have been proposed to tackle with spam web pages problem. In...
Feature selection has a significant role in the precision of text classification algorithms. In this regard, various approaches exist such as information Gain, Chi Square, Document Frequency, Mutual Information, etc. To improve the classification effectiveness combination of some input features may help a lot. In this paper, a new approach based on Ordered-Weighted Averaging (OWA) is proposed for...
The YAST (Yet Another Subspace Tracker) family algorithms were known for their exceptional convergence rate, compared to other subspace tracking algorithms. A proof for the numerical stability, in respect to orthogonality, of the last version of the YAST algorithm is presented in, and it is claimed that “a sudden loss of orthogonality” has made the previous implementations of this algorithm to diverge...
Face Recognition plays a vital role in automation of security systems; therefore many algorithms have been invented with varying degrees of effectiveness. After successful try out of principal component analyses (PCA) in eigenfaces method, many different PCA based algorithms such as Two Dimensional PCA (2DPCA) and Multilinear PCA (MLPCA), combined with several classifying algorithms were studied....
In this paper a hierarchical structure is proposed for automatic gender identification (AGI). In this structure two clustering techniques are used. The first technique is divisive clustering for dividing speakers from each gender to some classes of speakers. The second clustering technique is agglomerative clustering for creating a hierarchical structure. Feature reduction is done by SOAP feature...
In the field of pattern recognition, combination of different classifiers is a common method to improve classification accuracy. Recently, tendency to improve the function of clustering methods, specifically partitional clustering methods, is being increased. Generally, hierarchical clustering is preferred to partitional clustering when the number of exact clusters is undetermined or when we are interested...
In this paper, a new Hierarchical fuzzy classifier based on evolutionary boosting algorithms is proposed. The main goal of this paper is to improve the performance of fuzzy rule based classifiers through utilizing hierarchical structure for achieving fuzzy rules. The advantages of hierarchical fuzzy rules generated by evolutionary boosting algorithms are evaluated by comparison between the performance...
Machine learning can extract desired knowledge and ease the development bottleneck in building expert systems. Among the proposed approaches, deriving classification rules from training examples is the most common. Given a set of examples, a learning program tries to induce rules that describe each class. The rough-set theory has served as a good mathematical tool for dealing with data classification...
Recommender systems have become significant tools in electronic commerce, proposing effectively those items that best meet the preferences of users. A variety of techniques have been proposed for the recommender systems such as, collaborative filtering and content-based filtering. This study proposes a new hybrid recommender system that focuses on improving the performance under the “new user cold-start”...
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