When more than a single classifier has been trained for the same recognition problem the question arises how this set of classifiers may be combined into a final decision rule. Several fixed combining rules are used that depend on the output values of the base classifiers only. They are almost always suboptimal. Usually, however, training sets are available. They may be used to calibrate the ba...

On the Semantic Web, there has been increasing demand for a ruleslike expressivity that goes beyond OWL-DL. Efforts of combining rules languages and description logics usually produce undecidable formalisms, unless constrained in a specific way. We look at one of the most expressive but decidable such formalisms proposed: DL-safe rules, and present a tableaux-based algorithm for query answering...

Graph classification is an increasingly important step in numerous application domains, such as function prediction of molecules and proteins, computerised scene analysis, and anomaly detection in program flows. Among the various approaches proposed in the literature, graph classification based on frequent subgraphs is a popular branch: Graphs are represented as (usually binary) vectors, with c...

This paper bridges the gap between variable selection methods (e.g Pearson coefficients, KS test) and dimensionality reduction algorithms (e.g PCA, LDA). Variable selection algorithms encounter difficulties dealing with highly correlated data, as many features are similar in quality. Dimensionality reduction algorithms tend to combine all variables, and are not able to select significant variab...

This paper presents the word sense disambiguation system of Peking University which was designed for the SemEval-2007 competition. The system participated in the Web track of task 11 “English Lexical Sample Task via English-Chinese Parallel Text”. The system is a hybrid model by combining two supervised learning algorithms SVM and ME. And the method of entropy-based feature chosen was experimen...

Real life classification problems require an investigation of relationships between features in heterogeneous data sets, where different predictive models can be more proper for different regions of the data set. A solution to this problem is the application of the local boosting of weak classifiers ensemble method. A main drawback of this approach is the time that is required at the prediction...

MAVE (Multinet-based Answer Verification) is a system for answer validation which combines logic-based techniques and aggregation for identifying the correct answers in given sets of answer candidates. The paper explains the basic concepts underlying MAVE and also presents ablation studies which reveal the contribution of the proposed methods to the achieved quality of selection.

This article investigates the performance of combining support vector machines (SVM) and various feature selection strategies. Some of them are filtertype approaches: general feature selection methods independent of SVM, and some are wrapper-type methods: modifications of SVM which can be used to select features. We apply these strategies while participating at NIPS 2003 Feature Selection Chall...

A selection combining scheme is considered as an upgrade solution to existing wireless networks. The upgrade requires the use of a new radio frequency front end at the receiver with three antennas instead of one. An approach to guarding the system from diversity loss caused by antennas proximity is proposed. As an example, the upgrade of mobile units in an IS-136 network is analyzed. Performanc...

The severity of fading on mobile communication channels calls for the combining of multiple diversity sources to achieve acceptable error rate performance. Traditional approaches perform the combining of the different diversity sources using either: the Conventional Selective diversity combining (CSC), Equal-Gain combining (EGC), or MaximalRatio combining (MRC) Schemes. CSC and MRC are the two ...