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Training Radial Basis Function Neural Networks for Classification via Class-Specific Clustering
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Institute of Electrical and Electronics Engineers Inc.
, 2016 , Article)
In training radial basis function neural networks (RBFNNs), the locations of Gaussian neurons are commonly determined by clustering. Training inputs can be clustered on a fully unsupervised manner (input clustering), or ...
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
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Institute of Electrical and Electronics Engineers Inc.
, 2016 , Article)
Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such ...
Self-organizing binary encoding for approximate nearest neighbor search
(
European Signal Processing Conference, EUSIPCO
, 2016 , Conference Paper)
Approximate Nearest Neighbor (ANN) search for indexing and retrieval has become very popular with the recent growth of the databases in both size and dimension. In this paper, we propose a novel method for fast approximate ...
Limited random walk algorithm for big graph data clustering
(
SpringerOpen
, 2016 , Article)
Graph clustering is an important technique to understand the relationships between the vertices in a big graph. In this paper, we propose a novel random-walk-based graph clustering method. The proposed method restricts the ...
Analysis of High-Dimensional Phase Space via Poincaré Section for Patient-Specific Seizure Detection
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Institute of Electrical and Electronics Engineers Inc.
, 2016 , Article)
In this paper, the performance of the phase space representation in interpreting the underlying dynamics of epileptic seizures is investigated and a novel patient-specific seizure detection approach is proposed based on ...
Heart sound anomaly and quality detection using ensemble of neural networks without segmentation
(
IEEE Computer Society
, 2016 , Conference Paper)
Phonocardiogram (PCG) signal is used as a diagnostic test in ambulatory monitoring in order to evaluate the heart hemodynamic status and to detect a cardiovascular disease. The objective of this study is to develop an ...
Learning to rank salient segments extracted by multispectral Quantum Cuts
(
Elsevier B.V.
, 2016 , Article)
and third, multispectral approach is followed to generate multiple proposals instead of a single proposal as in Quantum Cuts. The proposed learn-to-rank algorithm is then applied to these multiple proposals in order to ...
Salient object segmentation based on linearly combined affinity graphs
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Institute of Electrical and Electronics Engineers Inc.
, 2016 , Conference Paper)
In this paper, we propose a graph affinity learning method for a recently proposed graph-based salient object detection method, namely Extended Quantum Cuts (EQCut). We exploit the fact that the output of EQCut is ...
Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks
(
IEEE Computer Society
, 2016 , Article)
Goal: This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. Methods: An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently ...
An optimized k-NN approach for classification on imbalanced datasets with missing data
(
Springer Verlag
, 2016 , Conference Paper)
In this paper, we describe our solution for the machine learning prediction challenge in IDA 2016. For the given problem of 2-class classification on an imbalanced dataset with missing data, we first develop an imputation ...