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We present an empirical study of gender classification of human faces, using new learning methodology called inference through contradictions, introduced in . This approach allows to incorporate a priori knowledge in the form of additional (unlabeled) samples, called the Universum, into the supervised learning process. Application of this methodology to gender classification shows that using this...
In this work we present our initial approach to the Recognizing Textual Entailment Search Pilot Task proposed by NIST. We proposed a new algorithm to address Text Entailment task to a document level making use of coreference resolution and then reducing this problem to a traditional main task problem. We also applied machine learning algorithms and a combination of datasets for the textual entailment...
A learning model of fuzzy formal language is proposed and discussed. We continue training the learning machine by giving sets of sentences sequentially. As a result of parsing of the given teaching sentences, the learning machine reinforces fuzzy grades of membership of productions in an inherent fuzzy grammar of the machine. The convergence of the proposed model is considered, and it is shown that...
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