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Deep CCA is a recently proposed deep neural network extension to the traditional canonical correlation analysis (CCA), and has been successful for multi-view representation learning in several domains. However, stochastic optimization of the deep CCA objective is not straightforward, because it does not decouple over training examples. Previous optimizers for deep CCA are either batch-based algorithms...
Support vector machine has obtained more and more attentions as a new method of machine learning based on the statistic learning theory. At the same time, there are increasing concerns about the fault diagnosis for practical engineering systems. Firstly, many kinds of SVM algorithms will be introduced, such as LS-SVM, LSVM and PSVM and so on. Besides, the advantages and disadvantage of those methods...
We pursue a time-domain feedback analysis of adaptive schemes with nonlinear update relations. We consider commonly used algorithms in blind equalization and neural network training and study their performance in a purely deterministic framework. The derivation employs insights from system theory and feedback analysis, and it clarifies the combined effects of the step-size parameters and the nature...
This paper presents an investigation into the detection and classification of drum sounds in polyphonic music and drum loops using non-negative matrix deconvolution (NMD) and the Itakura Saito divergence. The Itakura Saito divergence has recently been proposed as especially appropriate for decomposing audio spectra due to the fact that it is scale invariant, but it has not yet been widely adopted...
Given to the non-line-of-sight (NLOS) error existing in the location of wireless sensor network (WSN), together with the strong anti-noise ability, good data approximation and flexible parallel data processing ability of BP neural network, the method of using BP neural network to optimize the location of WSN nodes is put forward in this paper. Firstly, the source of error is analyzed. Then the traditional...
Many tasks in data mining and related fields can be formalized as matching between objects in two heterogeneous domains, including collaborative filtering, link prediction, image tagging, and web search. Machine learning techniques, referred to as learning-to-match in this paper, have been successfully applied to the problems. Among them, a class of state-of-the-art methods, named feature-based matrix...
Information Retrieval (IR) is concerned with indexing and retrieving documents including information relevant to a user's information need. Relevance Feedback (RF) is an effective technique for improving IR and it consists of gathering further data representing the user's information need and automatically creating a new query. As RF relies on the ability of an IR system to learn new queries and is...
We consider the dictionary learning problem in sparse representations based on an analysis model with noisy observations. A typical limitation associated with several existing analysis dictionary learning (ADL) algorithms, such as Analysis K-SVD, is their slow convergence due to the procedure used to pre-estimate the source signal from the noisy measurements when updating the dictionary atoms in each...
A good web selection algorithm can provide the most suitable service for users. However, known for its slow convergence rate and proneness of oscillation in its learning process, the traditional error back propagation neural network algorithm cannot be applied in the service selection scenarios of actual smart distribution grid. In order to meet the requirements of telecommunication technology for...
This paper presents a multi-iterative tracking method using meanshift algorithm based on kalman filter in order to quicken the relatively slow convergence in the original tracking system, on the condition that kalman filter based meanshift algorithm has been widely used as a methodology of object tracking. The specific number of iteration in meanshift and kalman filter, which m and n are used respectively...
In indoor environment, there are gross errors in random measured values of base station, which has effect on generalization ability of BP neural network and then results in low location accuracy. In order to improve location accuracy, location algorithm of BP Neural Network based on residual analysis is proposed, namely conducting pretreatment on measured values separately in training phase and location...
Iterative learning control (ILC) algorithms are typically used to iteratively refine the feed-forward control input to a system to achieve an optimized performance objective. Because of its ease of implementation and robustness, ILC has found widespread use in a variety of industrial applications. However, a key limitation of ILC is the requirement that learning has to be re-initiated for each new...
In the fields of computer version, text classification and biomedical informatics, it needs to find the joint feature among serval learning tasks. Generally, resent results show that it can be realized by solving a l2,1-norm minimization problem. However, due to the non-smoothness of the norm, solving the resulting optimization problem is always challenging. This thesis designs an augmented Lagrange...
In this paper we present an alternative procedure for the prediction of propagation path loss in urban environments, which is based on Artificial Neural Networks (ANN). The goal of this work is to synthesize and model ANNs which would require entering at the input nodes a detailed and the same time small amount of information about the propagation environment. We apply the Differential Evolution (DE)...
In this paper we compare the performance of back propagation and resilient propagation algorithms in training neural networks for spam classification. Back propagation algorithm is known to have issues such as slow convergence, and stagnation of neural network weights around local optima. Researchers have proposed resilient propagation as an alternative. Resilient propagation and back propagation...
SpikeProp is a supervised learning algorithm for spiking neural networks analogous to backpropagation. Like backpropagation, it may fail to converge for particular networks, parameters and datasets. However there are several behaviours and additional failure modes unique to SpikeProp which have not been explicitly outlined in the literature. These factors hinder the adoption of SpikeProp for general...
The artificial bee colony algorithm is a novel simulated evolutionary algorithm. The artificial bee colony algorithm has positive feedback, distributed computation and a constructive greedy heuristic convergence. Back propagation is a kind of feed forward neural network widely used in many areas, but it has some shortcomings, such as low precision solutions, slow search speed and easy convergence...
The traditional BP neural network training method processes the training dataset serially on one machine, so the efficiency is quite low. The massive data that need to be explored brings great challenge for BP neural network. The traditional serial training method of BP neural network will encounter many problems, such as costing too much time and insufficient memory to finish the training process...
Accurate load forecasting play a key role in economical use of energy and real time security analysis of system. Artificial Neural Network (ANN) model have been extensively implemented to produce accurate results for short-term load forecasting with time lead ranging from an hour to a week. In this paper a practical case of the small load area of a town getting supplied by 19 distribution feeders...
In this paper, the limitations of conventional BP algorithm was analyzed, and to fasten the learning velocity of neural network and enhance its generalization capability, the APSO (adaptive particle swarm optimization) algorithm was introduced into BP network for the optimization of its weights and thresholds. To overcome its 'early maturity', the variance operation was made on particles owing bigger...
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