A Design and Training Application for Deep Convolutional Neural Networks and Support Vector Machines Developed on MATLAB

Recently, deep learning approach has been used widely in order to enhance the recognition accuracy with different application areas. In this paper, both of deep convolutional neural networks (CNN) and support vector machines approach were employed in human action recognition task. Firstly, 3D CNN approach was used to extract spatial and temporal features from adjacent video frames. Then, support vector machines approach was used in order to classify each instance based on previously extracted features. Both of the number of CNN layers and the resolution of the input frames were reduced to meet the limited memory constraints. The proposed architecture was trained and evaluated on KTH action recognition dataset and achieved a good performance.

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A Coupling Support Vector Machines with the Feature Learning of Deep Convolutional Neural Networks for Classifying Microarray Gene Expression Data

Modern Approaches for Intelligent Information and Database Systems - Studies in Computational Intelligence ◽

10.1007/978-3-319-76081-0_20 ◽

2018 ◽

pp. 233-243 ◽

Cited By ~ 4

Author(s):

Phuoc-Hai Huynh ◽

Van-Hoa Nguyen ◽

Thanh-Nghi Do

Keyword(s):

Gene Expression ◽

Neural Networks ◽

Support Vector Machines ◽

Feature Learning ◽

Microarray Gene Expression Data ◽

Support Vector ◽

Deep Convolutional Neural Networks ◽

Microarray Gene Expression ◽

Vector Machines ◽

Microarray Gene

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Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks

Entropy ◽

10.3390/e22060688 ◽

2020 ◽

Vol 22 (6) ◽

pp. 688

Author(s):

Adrián Vázquez-Romero ◽

Ascensión Gallardo-Antolín

Keyword(s):

Neural Networks ◽

Support Vector Machines ◽

Convolutional Neural Networks ◽

Support Vector ◽

Final Decision ◽

Experimental Protocol ◽

Ensemble Averaging ◽

One Dimensional ◽

Vector Machines ◽

Detection Of Depression

This paper proposes a speech-based method for automatic depression classification. The system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is evaluated using the data and the experimental protocol provided in the Depression Classification Sub-Challenge (DCC) at the 2016 Audio–Visual Emotion Challenge (AVEC-2016). In the pre-processing phase, speech files are represented as a sequence of log-spectrograms and randomly sampled to balance positive and negative samples. For the classification task itself, first, a more suitable architecture for this task, based on One-Dimensional Convolutional Neural Networks, is built. Secondly, several of these CNN-based models are trained with different initializations and then the corresponding individual predictions are fused by using an Ensemble Averaging algorithm and combined per speaker to get an appropriate final decision. The proposed ensemble system achieves satisfactory results on the DCC at the AVEC-2016 in comparison with a reference system based on Support Vector Machines and hand-crafted features, with a CNN+LSTM-based system called DepAudionet, and with the case of a single CNN-based classifier.

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