Titlebook: Knowledge and Systems Engineering; Proceedings of the S Viet-Ha Nguyen,Anh-Cuong Le,Van-Nam Huynh Conference proceedings 2015 Springer Inte

闡明

Ngoc-Bao Nguyen,Vu-Hoang Nguyen,Thanh Ngo Duc,Duy-Dinh,Duc Anh Duongationsp?dagogischer Konzepte.Gesellschaften, die durch globale Entwicklungen, Migration und sozialen Wandel gepr?gt sind, stehen vor Herausforderungen, die den Zusammenhalt und Nachhaltige Entwicklung beeinflussen. Das Buch bietet eine Sammlung von Fachtexten, die aus sozialwissenschaftlichen, psych

節(jié)約

Seiki Akama,Jair Minoro Abe,Kazumi Nakamatsuationsp?dagogischer Konzepte.Gesellschaften, die durch globale Entwicklungen, Migration und sozialen Wandel gepr?gt sind, stehen vor Herausforderungen, die den Zusammenhalt und Nachhaltige Entwicklung beeinflussen. Das Buch bietet eine Sammlung von Fachtexten, die aus sozialwissenschaftlichen, psych

fastness

絕緣

Interval Analysis for Decision Aidings which is based on the idea that the variability of the data is not always caused by the error but by the intrinsic variety of the systems outputs. From this idea, several methods for data analysis have been proposed and obtained results in a different point of view from the conventional analysis.

觀察

壓迫

Discriminative Prediction of Enhancers with Word Combinations as Features to predict enhancers using discriminative classifiers with generic sequence features such as k-mers or words. The accuracy of such discriminative prediction largely depends on the ability of the models to capture not only the presence of predictive k-mers (words), but also spatial constraints on cl

刪減

Improving Acoustic Model for Vietnamese Large Vocabulary Continuous Speech Recognition System Using p bottleneck features (DBNFs) is able to achieve significant improvements over a number of base bottleneck features which was reported previously. The experiments are carried out on the dataset containing speeches on Voice of Vietnam channel (VOV). The results show that adding tonal feature as input

Reclaim

legislate

Cougar

An Effective NMF-Based Method for Supervised Dimension Reductione propose a sparse NMF-based method for supervised dimension reduction which aims to detect the particular topics of each class. Beside exploiting constraint convex combination of the hidden topics for each instance, our method separably learns among classes to extract interpretable and meaningful c