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In conventional echo stat network (ESN), the reservoir are randomly generated, then the spectral radius of the reservoir is scaled to lower than 1. In this method, only the necessary condition for echo state property (ESP) of ESN is satisfied while the sufficient condition is ignored, thus the ESN stability may not be ensured. In this paper, with the predefined singular values (smaller than 1), the...
Echo state network (ESN), a novel recurrent neural network, has a randomly and sparsely connected reservoir. Since the reservoir is very large, the collinearity problem may exist in ESN. To overcome this problem and get a sparse architecture, an adaptive lasso echo state network (ALESN) is proposed, in which the adaptive lasso algorithm is used to calculate the output weights. The proposed ALESN can...
Most techniques for dealing with imbalanced data classification in SVM-related methods are sampling, weighting and ensemble strategies, which aim at balancing the importance of different classes when computing the decision boundary. But how to choose and design an appropriate balance strategy is still a difficult problem. In this paper, we propose a between-class discriminant twin support vector machine...
We propose a fully convolutional neural network (FCNN) model for ice concentration estimation from dual-polarized SAR images. Our network contains 5 convolutional layers. Tested in the Gulf of Saint Lawrence during freeze-up, the proposed model is demonstrated to generate improved ice concentration estimates compared to a CNNs with similar structure.
Echo state network (ESN) is a powerful tool for nonlinear system modeling. However, the random setting of structure (mainly the reservoir) may degrade its estimation accuracy. To create the optimal reservoir for a given task, a novel ESN design method based on differential evolution algorithm is proposed. Firstly, the weight matrix of reservoir is constructed via the singular value decomposition (SVD)...
Support Vector Machine (SVM) is one of the most popular machine learning algorithms. An energy-efficient SVM classifier is proposed in this paper, where approximate computing is utilized to reduce energy consumption and silicon area. A hardware architecture with reconfigurable kernels and overflow-resilient limiter is presented. For different applications, different kernels can be chosen and configured...
Training a bottleneck feature (BNF) extractor with multilingual data has been common in low resource keyword search. In a low resource application, the amount of transcribed target language data is limited while there are usually plenty of multilingual data. In this paper, we investigated two methods to train efficient multilingual BNF extractors for low resource keyword search. One method is to use...
Gene expression profiles provide hidden biological knowledge and key information that can be used to distinguish different types of cancer. Due to their high dimensionality and redundancy, gene expression data are often preprocessed by dimensionality reduction (DR) methods. Conventional supervised DR methods use only labeled samples to train the model, leading to a limited performance due to small...
Query-by-Example Spoken Term Detection(QbE-STD) has been a hot research topic in speech recognition field. While template representation is the key composition part of QbE-STD, many researchers have been committed to developing effective template representations to obtain the better performance. Gaussian posteriorgram has been widely used due to that the GMM model which generates the Gaussian posteriorgram...
Currently, all kinds of Location-based Services (LBS) are gradually demanding more location information of mobile users. The WiFi-based indoor positioning technologies have been investigated intensely. However, both positioning accuracy and stability are often degraded by RSS detection variance between different devices. To solve the two problems, this paper proposes a compensation strategy for RSS...
K-nearest neighbor (KNN) is a popular classification algorithm with good scalability, which has been widely used in many fields. When dealing with imbalanced data, minority examples are given the same weight as majority examples in the existing KNN algorithm. In this paper, we pay more attention to the minority class than the majority class, and we increase the weight of minority class according to...
High-throughput experimental techniques have produced a large amount of human protein-protein interactions, making it possible to construct a large-scale human PPI network and detect human protein complexes from the network with computational approaches. However, most of current complex detection methods are based on graph theory which can't utilize the information of the known complexes. In this...
The study on point sources in astronomical images is of special importance, since most energetic celestial objects in the Universe exhibit a point-like appearance. An approach to recognize the point sources (PS) in the X-ray astronomical images using our newly designed granular binary-tree support vector machine (GBT-SVM) classifier is proposed. First, all potential point sources are located by peak...
Aiming at the shorting of the existing atrial fibrillation (AF) detection algorithms and improve the ability of intelligent recognition and extraction of AF signals. Recently, deep learning theory with massive data has been used on image, voice and other filed widely. In this paper, a method based on the stack sparse autoencoder neural network, a instance of deep learning strategy, was proposed for...
The aim of this study was to analyze Surface Electromyography (sEMG) of low back muscles of low back pain (LBP) patients when they performed different movements. We recruited eighteen female LBP patients and eighteen healthy female subjects. They performed forward and backward trunk bending with the maximum voluntary movement, while sEMG data were collected from the multifidus, external oblique and...
Nowadays, Internet Protocol Television (IPTV) is gradually replacing the traditional TV. IPTV Users require better experience. Therefore, media providers are interested in finding the key factors which influence the Quality of Experience (QoE), and it is necessary to find a model to predict the QoE. In this paper, we discuss the relationship between the status of IPTV set-top box and user's QoE. There...
Analog beamforming (ABF) provides a low-cost, high power efficiency solution for millimeter wave (mmWave) backhaul. Typical ABF algorithms rely on BF training to exploit the array gains using the Channel State Information (CSI). However, most existing ABF methods either produce a large dynamic range or low Signal-to-Noise Ratio (SNR) during the training, which deteriorate the BF gains in practical...
An restricted Boltzmann machine learning algorithm were proposed in the two-lead heart beat classification problem. ECG classification is a complex pattern recognition problem. The unsupervised learning algorithm of restricted Boltzmann machine is ideal in mining the massive unlabelled ECG wave beats collected in the heart healthcare monitoring applications. A restricted Boltzmann machine (RBM) is...
The daily interpretation of SAR sea ice imagery is very important for ship navigation and climate monitoring. Currently, the interpretation is still performed manually by ice analysts due to the complexity of data and the difficulty of creating fine-level ground truth. To overcome these problems, a semi-supervised approach for ice-water classification based on self-training is presented. The proposed...
We propose strategies for a state-of-the-art keyword search (KWS) system developed by the SINGA team in the context of the 2014 NIST Open Keyword Search Evaluation (OpenKWS14) using conversational Tamil provided by the IARPA Babel program. To tackle low-resource challenges and the rich morphological nature of Tamil, we present highlights of our current KWS system, including: (1) Submodular optimization...
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