نتایج جستجو برای: multiclass support vector machines classifier

تعداد نتایج: 894472  

In this study, a Brain-Computer Interface (BCI) in Silent-Talk application was implemented. The goal was an electroencephalograph (EEG) classifier for three different classes including two imagined words (Man and Red) and the silence. During the experiment, subjects were requested to silently repeat one of the two words or do nothing in a pre-selected random order. EEG signals were recorded by ...

2003
Yassine Ben Ayed Dominique Fohr Jean Paul Haton Gérard Chollet

Support Vector machines (SVM) is a new and very promising classification technique developed from the theory of Structural Risk Minimisation [1]. In this paper, we propose an alternative out-of-vocabulary word detection method relying on confidence measures and support vector machines. Confidence measures are computed from phone level information provided by a Hidden Markov Model (HMM) based sp...

Introduction: Reduction of sediment supply requires the implementation of soil conservation and sediment control programs in the form of watershed management plans. Sediment control programs require identifying the relative importance of sediment sources, their quantitative ascription and identification of critical areas within the watersheds. The sediment source ascription is involves two...

Journal: :International Journal of Computer and Communication Technology 2010

Journal: :International Journal of Computer Applications 2013

In this paper, a new method for extracting dynamic properties for High Impedance Fault (HIF) detection using discrete Fourier transform (DFT) is proposed. Unlike conventional methods that use features extracted from data windows after fault to detect high impedance fault, in the proposed method, using the disturbance detection algorithm in the network, the normalized changes of the selected fea...

Journal: :IEEE Transactions on Neural Networks 2004

Journal: :Fuzzy Sets and Systems 2003

Journal: :IEEE Signal Processing Letters 2012

Journal: :IEEE transactions on systems, man, and cybernetics 2022

Often, when dealing with real-world recognition problems, we do not need, and often cannot have, knowledge of the entire set possible classes that might appear during operational testing. In such cases, need to think robust classification methods able deal "unknown" properly reject samples belonging never seen training. Notwithstanding, existing classifiers date were mostly developed for closed...

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