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This paper presents a training method of log-linear model for statistical machine translation based on structural support vector machine. This method is designed to directly optimize parameters with respect to translation quality. By adopting maximum-margin principle of SVM, the MT model can learn from training samples with generalization capability. Experiments are carried out on a hierarchical phrase-based...
The task of classifying the semantic relation between two nominals in a sentence is quite challenging due to lack of a large amount of labeled data. Existing models of semantic relation classification were built on either synthetic training data generated from unlabeled data or hand-annotated training data. Meanwhile, previous work showed that the preposition and verb in the sentences indicate important...
Transmission tower occupies an important position in the event of transmission of electricity. The failure of transmission tower would cause serious economic losses. As a damage identification parameter, variation ratio of curvature mode has a great ability to damage location. In the field of damage location identification on transmission tower, variation ratio of curvature mode achieved good results...
The increase of malware that are exploiting the Internet daily has become a serious threat. The manual heuristic inspection of malware analysis is no longer considered effective and efficient compared against the high spreading rate of malware. Hence, automated behavior-based malware detection using machine learning techniques is considered a profound solution. The behavior of each malware on an emulated...
We introduce a facial expression recognition method, which incorporates a weight to the Local Binary Pattern (LBP), and generates solid expression features. Furthermore, we use Adaboost to select a small set of prominent features, which is used by the Support Vector Machine (SVM) to classify facial expressions efficiently. Experimental results demonstrate that our method outperforms the state-of-the-art...
Reducing power consumption has become a priority in microprocessor design as more devices become mobile and as the density and speed of components lead to power dissipation issues. Power allocation strategies for individual components within a chip are being researched to determine optimal configurations to balance power and performance. Modelling and estimation tools are necessary in order to understand...
We proposed fuzzy inference schemes to address the changes of the lighting environment problems: the illumination of the images captured from camera installed on a moving vehicle also varies from frame to frame. First, the input image is checked with a fuzzy inference method to evaluate the illumination conditions in order to apply appropriate preprocessing operations to get a better result. To overcome...
The failure of drill string in gas drilling has become a technical problem for drilling workers. In this paper, based on the analysis of drill string failure data at home and abroad using Statistical Learning Theroy and Support Vector Machine which have a very rapid development in recent years, a new predictive model of drill string failure has been established in gas drilling. Experimental results...
Most existing semi-supervised methods implemented either the cluster assumption or the manifold assumption. The performance will degrade if the assumption was not proper for the data. A method was proposed by combining both the cluster assumption and the manifold assumption. A semi-supervised kernel which reflected geometric information of the samples was constructed through warping the Reproducing...
Micro array data have a low instance-count and high dimensionality problem which prevent classifiers from building accurate models. This may result in significantly different classification accuracies across classifiers and features chosen. Therefore it is important to select the classifier and feature selection method that perform well on a specific data set. This paper proposes a novel criterion...
Gene expression (micro array) data have been used widely in bioinformatics. The expression data of a large number of genes from small numbers of subjects are used to identify informative biomarkers that may predict or help in diagnosing some disorders. More recently, increasing amounts of information from underlying relationships of the expressed genes have become available, and workers have started...
Development of a feature ranking method based upon the discriminative power of features and unbiased towards classifiers is of interest. We have studied a consensus feature ranking method, based on multiple classifiers, and have shown its superiority to well known statistical ranking methods. In a target environment such as a medical dataset, missing values and an unbalanced distribution of data must...
The problem of spam detection is a crucial task in the web information retrieval systems. The dynamic nature of information resources as well as the continuous changes in the information demands of the users makes the task of web spam detection a challenging topic. So far many different methods from researchers with different backgrounds have been proposed to tackle with spam web pages problem. In...
For last few years, researchers are increasingly employing machine learning methods in the domain of cancer prognosis. The main reason behind these efforts is to help oncologist to make accurate and less invasive decisions for the patient's treatment. Moreover, it would relieve many cancer patients from agonizingly complex surgical treatments and their colossal costs. In this paper, we have proposed...
Five different classification models, namely RFR_SUM, CRFs, Maximum Entropy, SVM and Semantic Similarity Model, are employed for polyphonic disambiguation. Based on observation of the experiment outcome of these models, an additional ensemble method based on majority voting is proposed. The ensemble method obtains an average precision of 96.78%, which is much better than the results obtained in previous...
This paper addresses the problem of sensor-based grasping under uncertainty, specifically, the on-line estimation of grasp stability. We show that machine learning approaches can to some extent detect grasp stability from haptic pressure and finger joint information. Using data from both simulations and two real robotic hands, the paper compares different feature representations and machine learning...
Graph propositionalization methods can be used to transform structured and relational data into fixed-length feature vectors, enabling standard machine learning algorithms to be used for generating predictive models. It is however not clear how well different propositionalization methods work in conjunction with different standard machine learning algorithms. Three different graph propositionalization...
The design of traffic sign recognition (TSR) system, one important subsystem of Advanced Driver Assistance System (ADAS), has been a challenge practical problem for many years due to the complex issues like road environments, lighting conditions, occlusion, and so on. In this paper, we introduce a new TSR system, whose effectiveness has been tested through extensive experiments. The established TSR...
Knowledge about protein-protein interactions unveils the molecular mechanisms of biological processes. This paper presents a multiple kernels learning-based approach to automatically extracting protein-protein interactions from biomedical literature. Experimental evaluations show that our approach can achieve state-of-the-art performance with respect to comparable evaluations, with 64.88% F-score...
This paper proposes a non-intrusive method to predict/estimate the intracranial pressure (ICP) level based on features extracted from multiple sources. Specifically, these features include midline shift measurement and texture features extracted from CT slices, as well as patient's demographic information, such as age. Injury Severity Score is also considered. After aggregating features from slices,...
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