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Using melody and/or lyric to query a music retrieval system is convenient for users but challenging for developers. This paper proposes efficient schemes for realizing key algorithms in such a kind of system. Specifically, we characterize our system by adding lyric to query as follows: A Support Vector Machine (SVM) is employed to distinguish humming queries from singing queries, For a singing query,...
This paper proposes a feature relation network (FRN) to model the underlying feature relation structures of a set of observations. A pattern classification system is then constructed based on the feature relation network, namely PCS-FRN. During training process, PCS-FRN will form an attractor for each group of samples in order to lower the overall energy states. The attractor, or a feature relation...
Based on the framework of support vector machines (SVM) using one against one (OAO) strategy, a new kernel method based on Bhattacharyya distance is proposed to raise the classification accuracy by combining the characteristics of hyperspectral data. The proposed method takes advantage of the non-uniform information distribution of hyperspectral data and makes the band with greater separability play...
Ontology learning aims to facilitate the construction of ontologies by decreasing the amount of effort required to produce an ontology for a new domain. However, there are few studies that attempt to automate the entire ontology learning process from the collection of domain-specific literature, to text mining to build new ontologies or enrich existing ones. In this paper, we present a complete framework...
Support vector machine (SVM) appears to be a robust alternative for pattern recognition with hyperspectral data. However, this kernel-based method does not take into consideration the bio-physical meaning of the spectral signatures. Observation of real-life spectral signatures from the AVIRIS hyperspectral dataset shows that the useful information for classification is not equally distributed across...
Ontology learning has become a major area of research whose goal is to facilitate the construction of ontologies by decreasing the amount of effort required to produce an ontology for a new domain. However, there are few studies that attempt to automate the entire ontology learning process from the collection of domain-specific literature, to text mining to build new ontologies or enrich existing...
The traditional intrusion detection system (IDS) generally use the misuse detection model based on rules because this model has low false alarm rate. But the disadvantage of this model is that it could not detect the new attacks, even the variation of existed ones. In this paper we proposed a novel model based on KPCA and SVM to solve the mentioned problem above. Different from traditional IDS, we...
Regarding to the daily load forecasting, the sample selection and data preprocessing are crucial to its' precision. In this paper, case-based reasoning (CBR) is adopted to search the historical data whose features are the same as the predict day. CBR is realized through the steps of case representation, indexing, retrieval, and adaptation, and the key idea in CBR involves the use of already existing...
The electronic mail (e-mail) concept makes it possible to communicate with many people in an easy and cheap way. Though email brought us such huge convenience, it also caused us trouble of managing the large quantities of spam mails received everyday. Without appropriate counter-measures, the situation seems to be worsening and spare email will eventually undermine the usability of email. To efficiently...
The goal of a text classification system is to determine whether a given document belongs to which of the predefined categories. An optimal SVM algorithm for text classification via multiple optimal strategies is proposed in this paper. The experimental results indicate that the proposed optimal classification algorithm yields much better performance than other conventional algorithms
This paper proposes a new document representation method to text categorization. It applies category-based semantic field (CBSF) theory for text categorization to gain a more efficient representation of documents. The lexical chain is introduced to compute CBSF and Hownet* used as a lexical database. In particular, the title of each document functions as a clue to forecast the potential CBSF of the...
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