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The greatest concern facing renewable energy sources like wind is the uncertainty in production volumes as their generation ability is inherently dependent on weather conditions. When providing forecasts for newly commissioned wind farms there is a limited amount of historical power production data, while the number of potential features from different weather forecast providers is vast. Bayesian...
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...
This paper introduces a multi-Bayesian framework for detection and classification of features in environments abundant with error-inducing noise. This approach takes advantage of Bayesian correction and classification in three distinct stages. The corrective scheme described here extracts useful but highly stochastic features from a data source, whether vision-based or otherwise, to aid in higher-level...
We present OCSB, a novel online Bayesian framework for imbalance multi-class data streams. To the best of our knowledge, OCSB is the first online method applying both cost-sensitive learning and sampling technique in a single classifier to deal with class imbalance learning. Specifically, an artificial cost matrix is designed and adapted in a sequential manner to not only boost the accuracy of minority...
This article provides a comparison of two feature selection algorithms, Information Gain Thresholding and Koller and Sahami's algorithm in the context of text document classification on the Reuters Corpus Volume 1 dataset. The algorithms were evaluated by testing the performance of classifiers trained on the features they select from a given dataset. Results show that Koller and Sahami's algorithm...
In this paper we consider centralized cooperative spectrum sensing (SS) techniques for cognitive radio networks using energy detector scheme. In light of the requirements imposed by centralized SS methods such as Maximum Ratio Combining (MRC), namely the estimation and transmission of the signal-to-noise ratio (SNR) on each secondary user, as well as the transmission of the exact energy level to the...
Word-sense disambiguation is one of the key concepts in natural language processing. The main goal of a language is to present a specific concept to the audience. This concept is extracted from the meaning of words in that language. System should be able to identify role and meaning of words in order to identify the concepts in texts properly. This issue becomes more problematic if there are words...
Committees of multilayer neural networks were used to estimate the appropriate surface area and thickness of RF absorbing material needed to achieve a desired quality factor (Q) inside a reverberation chamber. The networks were trained with Bayesian Regularization to avoid overfitting. Monte Carlo cross-validation was used to develop confidence bounds on the accuracy of the network committees.
The rapidly increasing number of elderly people has led to the development of in-home assistive robots for assisting and monitoring elderly people in their daily life. To these ends, indoor scene and human activity recognition is fundamental. However, image processing is an expensive process, in computational, energy, storage and pricing terms, which can be problematic for consumer robots. For this...
This paper considers the problem of knowledge-aided space-time adaptive processing (KA-STAP) combined with a parametric technique. Specifically, by modeling the disturbance as a multichannel autoregressive (AR) process, we introduce a stochastic signal model in which the spatial covariance matrix of the disturbance is assumed to be random, with some prior distribution. Incorporating the a priori knowledge...
Intrusion Detection Systems (IDSs) are powerful systems which monitor and analyze events in order to detect signs of security problems and take action to stop intrusions. In this paper, the Two Layers Multi-class Detection (TLMD) method used together with the C5.0 method and the Naive Bayes algorithm is proposed for adaptive network intrusion detection, which improves the detection rate as well as...
Shape models provide a compact parameterization of a class of shapes, and have been shown to be important to a variety of vision problems, including object detection, tracking, and image segmentation. Learning generative shape models from grid-structured representations, aka silhouettes, is usually hindered by (1) data likelihoods with intractable marginals and posteriors, (2) high-dimensional shape...
This paper presents an infinite variational autoencoder (VAE) whose capacity adapts to suit the input data. This is achieved using a mixture model where the mixing coefficients are modeled by a Dirichlet process, allowing us to integrate over the coefficients when performing inference. Critically, this then allows us to automatically vary the number of autoencoders in the mixture based on the data...
We propose to jointly learn a Discriminative Bayesian dictionary along a linear classifier using coupled Beta-Bernoulli Processes. Our representation model uses separate base measures for the dictionary and the classifier, but associates them to the class-specific training data using the same Bernoulli distributions. The Bernoulli distributions control the frequency with which the factors (e.g. dictionary...
In this paper, a new classifier under Bayesian framework is proposed to explore homogeneous region based low rank representation in hidden field for classification of hyperspectral imagery (HSI). This classifier integrates low rank representation and superpixel segmentation simultaneously, in which the HSI data is assumed to be lying in a low rank subspace within each homogeneous region of an estimated...
Virtual reality (VR) technology allows users to communicate or interact with a virtual artifact in an immersive simulated environment. Most of the existing virtual reality platform uses standard input devices such as a keyboard and mouse, or through multimodal devices such as a wired glove. In this paper, a novel electroencephalo-graph (EEG) based virtual driving (EEG-VD) prototype is proposed, investigated,...
A solution for the traditional Bayesian classification problem in non-traditional conditions is proposed, when the distributions and a priori probabilities of classes are unknown, but a trained sample from the zero class (labeled positive) and mixed sample (unlabeled) are available. Mixed sample will be employed in the learning to restore mixed distribution and as a test sample for constructed classifier...
In the complex pattern classification problem, the fusion of multiple classification results produced by different attributes is able to efficiently improve the accuracy. Evidence theory is good at representing and combining the uncertain information, and it is employed here. Each attribute (set) can be considered as one source of evidence (information). In some applications, the observation of target...
This paper describes a novel algorithm to improve the performance of sparsity based single-channel speech separation(SCSS) problem based on compressed sensing which is an emerging technique for efficient data reconstruction. The conventional approach assumes the mixing conditions and source signals are stationary. For practical applications of audio source separation, however, we face the challenges...
We propose a new semantic segmentation method and the necessity of certainty for practical use of semantic segmentation in scene understanding. We implement a deep fully convolutional encoder-decoder neural network for semantic segmentation. This network architecture makes the segmentation accuracy improve by retaining boundary details in the extracted image representation. This accuracy means how...
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