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The study was to compare principle component (PC) versus partial least square (PLS) regression, the former unsupervised and the latter supervised gene component analysis, for highly complicated and correlated microarray gene expression profile. Projection of derived classifiers into independent samples for clinical phenotype prediction was evaluated as well. Previous studies had suggested that PLS...
The monitoring and management of the high density crowd in large scale public place is an important factor of city disaster reduction and mitigation. Automatic short term prediction of crowd density is a key problem. This paper introduces a prediction algorithm using v-support vector regression (v-SVR), which can control the accuracy of fitness and prediction error by adjusting the parameter v. An...
This paper presents an alternative to the traditional impedance based fault location methods, using a simple technique of the learning approaches called k-Nearest Neighbors (k-NN), where besides the fault location distance, the multiple estimation problem is also addressed. This approach only uses the single end measurements of voltage and current available at the power substation. As principal advantage,...
In this paper we propose a Round Trip Translation (RTT) based approach to sentence-level confidence estimation (CE) for spoken language translation without the assistant of reference translations generated by human. A number of novel RTT based features are introduced to reflect the quality of spoken language translation in more detail. After combing various kinds of features together, support vector...
Control of interior permanent magnet (IPMSM) is difficult because its nonlinearity and parameter uncertainty. In this paper, a fuzzy c-regression models clustering algorithm which is based on T-S fuzzy is used to model IPMSM with a series linear model and weight them by memberships. Lagrangian of constrained function is built for calculating clustering centers where training output data are considered...
Slideshow is a popular way to display a digital album automatically. However, it is time consuming and boring when the album is large. Actually, while browsing an album manually, an audience may spend different length of time on different photos. Considering this, in this paper, we propose an approach to estimate the attention amount attracted by each photo and further predict its duration. Three...
Nonparametric Wilcoxon regressors, which generalize the rank-based Wilcoxon approach for linear parametric regression problems to nonparametric neural networks, were recently developed aiming at improving robustness against outliers in nonlinear regression problems. It is natural to investigate if the Wilcoxon approach can also be generalized to nonparametric classification problems. Motivated by...
Bus incident are random, while its prediction is a nonlinear time-varying system, and is difficult to use mathematical models to achieve accurate modeling .The paper presents a real-time forecasting method of bus incident based on a dynamic fuzzy-neural network .First, the paper defines what are called bus incident, and then analyzes the factors that will lead to bus incident. Introduce using cluster...
Interactive Neuro-Educational Technologies (I-NET) are designed to increase the pace and efficiency of skill learning by adapting training environments to the skill levels and needs of the individuals. Advanced Brain Monitoring (ABM) explored the feasibility of integrating physiological measures into an interactive adaptive computer-based training system to facilitate mitigations, accelerate skill...
How individual factors influence cyberloafing in enterprises has been studied with multiple regression method. Results showed that eight factors influence cyberloafing significantly, which were ideological cognition, personal habits, psychological dependence, making personal gain, interpersonal interaction, workplace status, inspiring creativity and degree of understanding of related regulations.
With the decrease of nature forest in all over the world, plantation and improving wood utilization becomes important. The study on forecast of wood properties can provide a scientific basis for intensive farming of plantation and targeted utilization of wood resource. The purpose of this paper is to establish a forecast model of wood properties and to evaluate wood qualities comprehensively. Tracheid...
It is of essentially importance for minor enterprises to promote the fast and healthy growth by developing, managing and utilizing the technology resources to compensate the shortage in talents and technology and by enhancing technological creativity and core competitiveness. With SPSS 15.0 software, we processed the sample statistics through the factor analysis on the five dimensions: enterprises'...
In this paper, we propose using an environment structuring framework to facilitate suitable prior density estimation for maximum a posteriori linear regression (MAPLR) under adverse testing conditions. The framework is constructed in a two-stage hierarchical tree structure by performing two algorithms, environment clustering and environment partitioning. The constructed framework has good capability...
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...
For developing forestry ecological economy is a most watched problem recent years, to analyze the relative economic data and find out existing problems, will greatly help governments revise the policies and promote rapidly development of forestry ecological economy. Based on the support vector regression theory, the thesis builds the regression analysis model of our forestry ecological development,...
To reduce the classification errors of online handwritten Japanese character recognition, we propose a method for confusing characters discrimination with little additional costs. After building confusing sets by cross validation using a baseline quadratic classifier, a logistic regression (LR) classifier is trained to discriminate the characters in each set. The LR classifier uses subspace features...
Presented a kind of principle and method based on regression support vector machine dynamic data significant error detection. The method takes full advantage of the nonlinear approximation capability supporting vector machine. The establishment of nonlinear system dynamic process model convex to a quadratic twice optimization problem, which can be guaranteed the extremal solution is global optimal...
Machine Learning deals with the issue of how to build programs that improve their performance at some task through experience. This paper deals with the subject of applying machine learning methods to software engineering. For effort estimation which not only provide an estimation but also confidence interval for it. The robust confidence intervals do not depend on the form of probability distribution...
Video scene segmentation and classification are fundamental steps for multimedia retrieval, browsing and indexing. In this paper, we present a robust scene segmentation approach based on the Markov Chain Monte Carlo (MCMC) method using the structure of video sequences. In our method, there are two novel approaches to segment video sequences into scenes. The first approach is the use of the video structures...
The Covering algorithm is proposed by Professor ZhangLing and ZhangBo in the 20th century, which simulates the structure of human learning, building a Constructive Neural Network Learning Model. Covering algorithm has been widely used to solve massive data classification problem, because its performance. The covering classification algorithm has fast learning, high recognition rate, massive data processing...
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