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A range of algorithms was used to classify online retail customers of a UK company using historical transaction data. The predictive capabilities of the classifiers were assessed using linear regression, Lasso and regression trees. Unlike most related studies, classifications were based upon specific and marketing focused customer behaviours. Prediction accuracy on untrained customers was generally...
Aspect-based sentiment analysis has always been a difficult task since it consists of several core sub-tasks: feature detection, opinion extraction and polarity classification. Consequently, by now there is little work to summarize all of these works together. In this paper, we propose a brand new holistic system, which can deal with all the problems above simultaneously using aspect-based positive...
In order to analyze the economic performance of thermal power plant, a partial least squares support vector machine coupling model was constructed. First of all, the coal consumption rate was selected as the evaluating indicator, which is an important indicator to evaluate the economy of power plant. At the same time, the physical quantities were established, which is closely related to coal consumption...
Pattern classification in domains that follow dissimilar distribution and where target domain has insufficient labelled samples, requires transfer of knowledge across domains through a process called domain adaption. Deep learning research demonstrates the transferability of deep convolutional features that are activations of intermediate layers of convolutional neural networks for domain adaption...
Decision making is an important component in a speaker verification system. For the conventional GMM-UBM architecture, the decision is usually conducted based on the log likelihood ratio of the test utterance against the GMM of the claimed speaker and the UBM. This single-score decision is simple but tends to be sensitive to the complex variations in speech signals (e.g. text content, channel, speaking...
In this paper we present a novel writer-independent on-line signature verification system. The proposed system obtains a universal signature representation in dissimilarity space, but remains able to compensate for the personal variability of an individual's handwriting by subsequently employing a writer-specific dissimilarity normalisation strategy. Signature modelling is achieved by utilising either...
Artificial neural networks (ANNs) have rarely been used in the field of medical education, especially in the prediction of learning performances. This study aims to evaluate the potential application of ANN models for predicting learning performances, in comparison with multivariate logistic regression models. The predictor variables included demographics, high-school backgrounds, first-year grade-point...
Twitter sentiment analysis provides organizations with real-time monitoring of public feelings towards particular products and events related to them. Most existing research is focused on extraction of sentiment features through analysis of lexical and syntactic features that are expressed explicitly through words, emoticons, exclamation marks etc. Single machine learning classifiers are usually employed...
In recent years, the web has become one of the main carrier of social public opinion, so the network public opinion needed a new natural language processing technology to identify the information efficiently and accurately, analysis and classify the public opinions effectively. In this paper, the text semantic orientation analysis method is proposed based on Hidden Markov Model to improve the accuracy...
Sentiment analysis is a technology with great practical value, it can solve the phenomenon of network comment information disorderly to a certain extent, and accurate positioning of user information required. Currently for Chinese sentiment analysis research is relatively small, including a variety of supervised learning method of classification result and the text feature representation methods and...
In this paper, we study the problem of learning from label proportions in which label information of data is provided in bag level. In this kind of problem, training data is grouped into various bags and only the proportions of positive instances is known. Inspired by proportion-SVM, we propose a new classification model based on twin SVM, which is also in a large-margin framework and only needs to...
Forecasting has become a very essential skill for all those, related to finance. Further, the advent of data mining tool and analytical technologies has changed the way to explore historical data for investment and managerial decision making. The current paper deals with two established technique viz. Epsilon-SVR and Decision Tree for stock market forecasting. The available numerical historical data...
In this paper, we develop the max-margin similarity preserving factor analysis (MMSPFA) model. MMSPFA utilizes the latent variable support vector machine (LVSVM) as the classification criterion in the latent space to learn a discriminative subspace with max-margin constraint. It jointly learns factor analysis (FA) model, similarity preserving (SP) term and max-margin classifier in a united Bayesian...
Statistical shape models generally characterize shape variations linearly by principal component analysis (PCA), which assumes that the non-rigid shape parameters are drawn from a Gaussian distribution. This practical assumption is often not valid. Instead, we propose a constrained local model based on independent component analysis (ICA) and use kernel density estimation (KDE) for non-parametrically...
Engine testing technology has made great development and gathering the engine data of failure becomes more and more easily. The engine fault recognition method based on data driving has made rapid development. The support vector machine (SVM) is currently a well-known machine learning technique. It has been applied to deal with range of fault recognition problems due to its unique advantages. There...
Traditional support vector machine treats all samples using the same weight. Therefore it is very sensitive to noisy data. While the fuzzy support vector machine assigns lower weights to the samples which make small contributions to classification, thus it is beneficial to reduce the effects of noisy and unimportant data on the classification accuracy rate. In this paper, we propose a novel fuzzy...
Graphics processing units (GPUs) can deliver considerable performance gains over general purpose processors. However, GPU performance improvement vary considerably across applications. Porting applications to GPUs by rewriting code with GPU-specific languages requires significant effort. In consequence, it is desirable to predict which applications would benefit most before porting to the GPU. This...
According to the characteristics of post evaluation for the productive capacity construction project of oilfield and the actual situation of oilfield, detailed analyzed the evaluation index, the relationship and the impact to the Comprehensive Post Evaluation of the post evaluation for the productive capacity construction project of oilfield, proposed the comprehensive post evaluation model based...
For the life of the aircraft air refrigerator trend analysis problems, this paper proposed the research method that based on a combination of SVM and neighborhood rough set attribute reduction. First, to find out the core factors which are the most significant ones affecting the life of the aircraft air refrigerator, the original information of the data is mined by the attribute reduction algorithm...
Opinions are highly essential for decision making and popular among the internet users. People with malicious intentions tend to give fake reviews to encourage or degrade the products. Reviewing movies is gaining popularity among web users, at the same time cannot be trusted. In this work, we propose a model Sentiment Classification of Movie Reviews using Efficient Repetitive Pre-processing (SentReP)...
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