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Linear Discriminant Analysis (LDA) is widely-used for supervised dimension reduction and linear classification. Classical LDA, however, suffers from the ill-posed estimation problem on data with high dimension and low sample size (HDLSS). To cope with this problem, in this paper, we propose an Adaptive Wishart Discriminant Analysis (AWDA) for classification, that makes predictions in an ensemble way...
Data mining plays an efficient role in prediction of diseases in health care industry. Diabetes is one of the major global health problems. According to WHO 2014 report, around 422 million people worldwide are suffering from diabetes. Diabetes is a metabolic disease where the improper management of blood glucose levels led to risk of generating abnormalities in functioning of critical organs like...
A general approach is proposed to determine the occupancy in a room using sensor data and knowledge coming respectively from observation and questioning are determined. Means to estimate occupancy include motion detections, power consumption and and acoustic pressure rewarded by a micro-phone. The proposed approach is inspired from machine learning. It starts by determining the most useful measurements...
The power sector faces a considerable loss of energy both technical and non-technical. The non-technical losses are related with energy delivered but whose cost is not recovered. Several attempts have made to minimize this problem, however the problem has persisted. The application of data mining algorithms to commercial and technical databases allows us to have patterns of energy consumption which...
In recent years, one mode of data dissemination has become extremely popular, which is the deep web. A key characteristics of deep web data sources is that data can only be accessed through the limited query interface they support. This paper develops a methodology for mining the deep web. Because these data sources cannot be accessed directly, thus, data mining must be performed based on sampling...
Extracting and analyzing the interesting patterns from spatio-temporal databases, have drawn a great interest in various fields of research. Recently, a number of experiments have explored the problem of spatial or temporal data mining, and some clustering algorithms have been proposed. However, not many studies have been dealing with the integration of spatial data mining and temporal data mining...
As more and more multimedia data become available on the Web, mining on those data is playing an increasingly important role in Web applications. In this paper, we investigate the interplay between multimedia data mining and text data mining. Specifically, in an approach we called text-aided image classification (TAIC), we address the problem of image classification with very limited amount of labeled...
It is well known that the key of Bayesian classifier learning is to balance the two important issues, that is, the exploration of attribute dependencies in high orders for ensuring a sufficient flexibility in approximating the ground-truth dependencies, and the exploration of low orders for ensuring a stable probability estimate from limited training samples. By allowing one-order attribute dependencies,...
Quantile regression refers to the process of estimating the quantiles of a conditional distribution and has many important applications within econometrics and data mining, among other domains. In this paper, we show how to estimate these conditional quantile functions within a Bayes risk minimization framework using a Gaussian process prior. The resulting non-parametric probabilistic model is easy...
Learning Bayesian networks can be examined as the combination of parameter learning and structure learning. Parameter learning is estimation of the conditional probabilities (dependencies) in the network. Structural learning is the estimation of the topology (links) of the network. The structure of the network can be known or unknown, and the variables can be expressed as complete and incomplete data...
Under the linear loss, we consider the test problem of the life parameter in the exponential distribution using empirical Bayes (EB) approach and present a monotone EB test possessing a rate of convergence which can be arbitrarily close to O(n-1) under the condition that the past samples are S\phiS-mixing.
Naive Bayes Classifiers have been known with the advantages of high efficiency and good classification accuracy and they have been widely used in many domains. However, the classifiers need complete data. And the phenomenon of missing data widely exists in practice. Facing this instance, learning naive Bayes classifier and classification method with missing data are built in this paper. Compared with...
This paper describes a particle filter based approach for estimating the ground plane from an image sequence. Based on a Bayesian framework, the particle filter provides a robust estimation of the plane parameters, since it can handle non-linearities, while allowing a high flexibility for integrating new cues into the system. Furthermore, the different modes of the resulting probability density function...
In this study, we propose a gradual adaption model for a Web recommender system. This model is used to track users' focus of interests and its transition by analyzing their information access behaviors, and recommend appropriate information. A set of concept classes are extracted from Wikipedia. The pages accessed by users are classified by the concept classes, and grouped into three terms of short,...
In this paper, we propose a robust approach to access point (AP) selection problem for the indoor location tracking. It takes the environments changes into account and makes use of residuals ranking algorithm to select those APs least sensitive to the environment changes in indoor location tracking, we call it ResidualRanking method, also we make an improvement of residual computing according to the...
Emotions that are elicited in response to a video scene contain valuable information for multimedia tagging and indexing. The novelty of this paper is to introduce a Bayesian classification framework for affective video tagging that allows taking contextual information into account. A set of 21 full length movies was first segmented and informative content-based features were extracted from each shot...
We consider the problem of parameter estimation of Markovian models where the exact computation of the partition function is not possible or computationally too expensive with MCMC methods. The main idea is then to approximate the expression of the likelihood by a simpler one where we can either have an analytical expression or compute it more efficiently. We consider two approaches: Variational Bayes...
It is well known that spatial perception is a basic ability in our daily life, while we compute spatial relationship between two objects universally. This study examined how people perceive spatial categories using three tasks, learning task, producing task, and rating task. Three different kinds of spatial configurations were manipulated. 27 subjects were assigned randomly to each kind of spatial...
A framework for a new type of estimation of distribution algorithms (EDAs) is developed. It is similar to the Bayesian optimization algorithm (BOA) except that it replaces Bayesian network model with estimation of schema distribution based on maximum entropy. As structure learning of Bayesian network is not needed, it reduces the computational cost. The experimental results show that the new algorithms...
In this paper, we present a new voice conversion method based on the state-space model (SSM). A modified version of the conventional SSM model is first proposed to describe the relationship between the source speech and the target speech in the spectral domain. Then the expectation maximum (EM) and variational Bayesian (VB) algorithms are individually employed to estimate the SSM parameters, resulting...
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