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Alzheimer's disease (AD) is one major cause of dementia. Previous studies have indicated that the use of features derived from Positron Emission Tomography (PET) scans lead to more accurate and earlier diagnosis of AD, compared to the traditional approach used for determining dementia ratings, which uses a combination of clinical assessments such as memory tests. In this study, we compare Naïve Bayes...
The source data of intrusion detection system (IDS) are characteristic of heavy-flow, high-dimension and nonlinearity. A frequent problem in IDS is the choice of the right features that give rise to compact and concise representations of the network data; the other is how to improve the detection efficiency and accuracy of IDS under the small sample conditions. In order to delete the redundant and...
Although many region based models for image auto-annotation have been proposed recently, their performances are not satisfactory due to the sensitivity to segmentation errors. In this paper, by evaluating two image partition methods and four visual features, we propose a new ensemble method under Multi-Instance Multi-Label (MIML) learning framework which has been proposed recently. The ensemble method...
Real life datasets often suffer from the problem of class imbalance, which thwarts supervised learning process. In such data sets examples of positive (minority) class are significantly less than those of negative (majority) class leading to severe class imbalance. Constructing high quality classifiers for such imbalanced training data sets is one of the major challenges in machine learning, since...
This article presents two classifiers based on machine learning methods, aiming to detect physiologic anomalies considering Poincaré plots of heart rate variability. It was developed a preprocessing procedure to encoding the plots, based on the Cellular Features Extraction Method. Simulation of different classifiers, artificial neural networks and support vector machine, has been performed and the...
In order to reduce the relativity and improve the separability of prototype pattern vectors, a spectral-based synergetic network learning algorithm is proposed in this paper. The most attractive feature of the new method is that its complexity is linear with data dimension. To approximate the optimal cut and prevent instability due to information loss, all eigenvectors are used. The eigenvalues and...
In order to reduce computer storage requirements for kernel matrix and the computational costs for floating point operations in kernel machine learning, compactly supported radial basis function is used for kernel machine to construct sparse kernel matrix. This paper deals with evaluation and comparison of compactly supported radial basis function for kernel machine in three aspects: the savings in...
Wireless sensor networks are designed to perform on inference the environment that they are sensing. Due to the inherent physical characteristics of systems under investigation, non-negativity is a desired constraint that must be imposed on the system parameters in some real-life phenomena sensing tasks. In this paper, we propose a kernel-based machine learning strategy to deal with regression problems...
Support Vector Machine (SVM) is a useful technique for data classification with successful applications in different fields of bioinformatics, image segmentation, data mining, etc. A key problem of these methods is how to choose an optimal kernel and how to optimize its parameters in the learning process of SVM. The objective of this study is to propose a Genetic Algorithm approach for parameter optimization...
Image super-resolution reconstructions (SR) require image degradation model (DM) as the prior, however, the actual DM is often unknown in practical applications. In this work, a novel framework is proposed for single image SR, where the explicit DM is unknown. Based on Bayesian MAP, an iteration scheme is adopted to update the reconstructed SR image and the DM estimate. During reconstruction, MRF-Gibbs...
Accurate traffic flow forecasting is crucial to the development of intelligent transportation systems (ITS). Based on statistical learning theory, support vector machine (SVM) has better generalization performance and can guarantee global minima for given training data. However, the good generalization performance of SVM highly depends on the construction of kernel function. An effective multi-scale...
Nucleosome, a nucleoprotein structure formed by coiling 147bp of DNA around an octamer of histone proteins, is the fundamental repeating unit of eukaryotic chromatin. By regulating the access of biological machineries to underlying \textit{cis}-regulatory elements, its mobility has been implicated in many important cellular processes. Although it has been known that various factors, such as DNA sequences,...
Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature selection techniques have been proposed for the traditional settings, where each instance is expected to have a label. In multiple instance learning (MIL) each example or bag consists of a variable set of instances, and the...
An important, yet under-explored, problem in pattern recognition concerns learning from data labeled at varying levels of specificity. The majority of existing machine learning methods are based on the inductive learning paradigm, where a labeled training set (one label per training example) trains a classifier which is markedly different from the human learning experience, where any one object can...
Boolean matching for multiple-output functions determines whether two given (in)completely-specified function vectors can be identical to each other under permutation and/or negation of their inputs and outputs. Despite its importance in design rectification, technology mapping, and other logic synthesis applications, there is no much direct study on this subject due to its generality and consequent...
This paper proposes an online functional test selection approach based on novelty detection. Unlike other test selection methods, the idea of this paper is selecting novel functional tests to improve coverage from a large pool of available test programs before simulation. A graph based encoding scheme is developed to measure the similarity between test programs and map them into a set of feature vectors...
Support Vector Machines, a new generation learning system based on recent advances in statistical learning theory deliver state-of-the-art performance in real-world applications such as text categorization, hand-written character recognition, image classification, bio-sequence analysis etc for the classification and regression. This paper emphasizes the classification task with Support Vector Machine...
In this paper, we proposed a novel semi-supervised learning algorithm, named passive-aggressive semi-supervised learner, which consists of the concepts of passive-aggressive, down-weighting, and multi-view scheme. Our approach performs the labeling and training procedures iteratively. In labeling procedure, we use two views, known as teacher's classifiers for consensus training to obtain a set of...
Locally weighted learning (LWL), which is an effectual and flexible method for prediction problems, is widely used in many regression scenarios. The training data samples, referring to the history experience knowledge base, are required to help do regression by new queries. However, sometimes, the knowledge base tends to be helpless due to the lake of information, such as inadequate training data...
An approach based on multi-class support vector machine (SVM) is put forward for pre-warning enterprise financial crisis. On the basis of financial crisis pre-warning index system, the actual situations of enterprises' financial crisis are used as learning samples to train and test the learning machine through cross-validation and one to one vote methods and all the optimal hyper plane functions of...
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