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Community Question Answering (CQA) has become a popular and effective mean for seeking information on the Web. It is now possible and effective to post a question asked in natural language on a popular community Question Answering (QA) portal, and to rely on other users to provide answers. These online collaborative services are attracting users and questions at an explosive rate, while how to correctly...
The identification of disease-related microRNAs is vital for understanding the pathogenesis of disease at the molecular level and may lead to the design of specific molecular tools for diagnosis, treatment and prevention. Experimental identification of disease-related microRNAs poses difficulties. Computational prediction of microRNA-disease associations is one of the complementary means. However,...
In this paper, a unified signal processing and machine learning method to automatically process Electrocardiogram (ECG) signal for classification of heartbeat type is presented. The method is divided into three stages: signal processing and transformation, feature extraction, and classification. The method can classify a beat into one of eight classes. Thirty features are extracted from time and frequency...
In this paper, a new fuzzy adaptive local modeling method based on local learning and weighted least squares support vector machine (LS-SVM) is proposed by building fuzzy membership model for the training data. Just as LSSVM, local LS-SVM is also sensitive to outliers or noises. A proper fuzzy membership model based on support vector data description (SVDD) is proposed to deal with the problem. Fuzzy...
The paper proposed a novel method for lip recognition based on Active Basis Model (ABM). There are four stages in a flowchart of this novel method. At the first stage the deformable templates of lip images is obtained. The lip images of deformable templates are obvious open or closed. The second stage is to obtain the deformed template of each testing images. The third stage, the difference between...
The Conformal Predictions framework is a recent development in machine learning to associate reliable measures of confidence with results in classification and regression. This framework is founded on the principles of algorithmic randomness (Kolmogorov complexity), transductive inference and hypothesis testing. While the formulation of the framework guarantees validity, the efficiency of the framework...
We propose a novel algorithm for greedy forward feature selection for regularized least-squares (RLS) regression and classification, also known as the least-squares support vector machine or ridge regression. The algorithm, which we call greedy RLS, starts from the empty feature set, and on each iteration adds the feature whose addition provides the best leave-one-out cross-validation performance...
Recently, the so-called Support Feature Machine (SFM) was proposed as a novel approach to feature selection for classification, based on minimisation of the zero norm of a separating hyper plane. We propose an extension for linearly non-separable datasets that allows a direct trade-off between the number of misclassified data points and the number of dimensions. Results on toy examples as well as...
The identification of optimal candidates for ventricular assist device (VAD) therapy is of great importance for future widespread application of this life-saving technology. During recent years, numerous traditional statistical models have been developed for this task. In this study, we compared three different supervised machine learning techniques for risk prognosis of patients on VAD: Decision...
MicroRNAs are one type of noncoding RNA that regulate their target mRNAs before mRNAs are translated into proteins. Although it has been demonstrated that the regulation is through partial binding of the seed region of a miRNA and its targets, the mechanism of this process is not fully discovered. Some biological experiments have shown that even perfect base pairing in the seed region does not always...
ATP is a ubiquitous nucleotide that provides energy for cellular activities, catalyzes chemical reactions, and is involved in cellular signaling. The knowledge of the ATP-protein interactions helps with annotation of protein functions and finds applications in drug design. We propose a high-throughput machine learning-based predictor, ATPsite, which identifies ATP-binding residues from protein sequences...
Cryptanalysis attempts identify the weaknesses in the algorithms used to encrypt code or the methods used to generate keys. In this study, we use pattern recognition techniques for identification of encryption algorithms for block ciphers. The following block cipher algorithms, DES, IDEA, AES, and RC operating in ECB mode were considered. Eight different classification techniques which are: Naïve...
Support Vector Machines (SVMs) are a powerful supervised learning tool, providing state-of-the-art accuracy at a cost of high computational complexity. The SVM classification suffers from linear dependencies on the number of the Support Vectors and the problem's dimensionality. In this work, we propose a scalable FPGA architecture for the acceleration of SVM classification, which exploits the device...
Multi-scale kernel function learning is a special case of multi-kernel learning, namely combines several multi-scale kernels. This approach is more flexible. It provides more comprehensive choice of scale than the mixed kernel learning. In this paper, the model's parameters of multi-scale Gaussian kernel were used as elementary particles. The parameters of multi-scale Gaussian kernel were global optimized...
Attribute dependency function is very important for feature selection in data mining, pattern recognition and machine learning. However, Pawlak's is inadequate for some information systems, and Daisuke's definition is only for categorical attribute. In this paper, we introduce a new definition based on partition for numerical attribute. The advantage of the definition is that heterogeneous features...
With the rapid development of educational technologies, machine learning (ML) based second language learning (SLL) attracts the attention of many scholars from computational linguistics. Garden path (GP) sentence is a special sentence structure in which processing breakdown and backtracking are involved in the machine decoding. Faced with GP sentence, learners have to make original misinterpretation...
In dealing with a large number of train samples, Support Vector Regression (SVR) algorithm is slow. In particular, while new sample is added, all the training samples must be re-trained. In this paper, a new SVR incremental algorithm is presented, which is based on boundary vector. The algorithm takes full advantages of the geometric information of training sample sets. The observed data of China's...
Abstract-Prediction of protein-proteininteraction sites is very important to the function of a protein and drug design. In this paper, we adequately utilize the characters of ensemble learning, which can improve the accuracy of individual classifier and generalization ability of the system, and propose a new prediction method of protein-protein interaction sites: ensemble learning method based on...
Business development is vital for any firms. However, globalization and the rapid development of technologies have made it difficult to find appropriate business partners such as suppliers, customers and outsources. In this contribution, we propose a new computational approach to find business partner candidates based on firm profiles and transactional relationships among them. We employ machine learning...
This paper describes the control of an autonomous biped robot that combines the use of the torso and the ankle joints movements for its sagittal balance. The innovative characteristic of this controller is the combined use of the ankle and torso joints movements to correct the Zero Moment Point (ZMP). It is used an artificial intelligence technique, the Support Vector Regression, to control the balance...
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