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The problem of construction the neuronetworking systems for non-stationary information adaptive processing at various practical applications is formulated. The developed methods and algorithms of neural network training subset formation allow to take into account the conditions of information transfer, variation of statistical parameters and dynamic properties of data. The controlling algorithms which...
This paper introduces a solution to enable self-optimization of coverage and capacity in LTE networks through base stations' downtilt angle adjustment. The proposed method is based on fuzzy reinforcement learning techniques and operates in a fully distributed and autonomous fashion without any need for a priori information or human interventions. The solution is shown to be capable of handling extremely...
Troubleshooting of wireless networks is a challenging network management task. We have developed, in a previous work, a new troubleshooting methodology, which we named Statistical Learning Automated Healing (SLAH). This methodology uses statistical learning, in particular logistic regression, to extract the functional relationships between the noisy Key Performance Indicators (KPIs) and Radio Resource...
This paper deals with time-optimization of trajectories of wheeled robots within the speed and other constraints. The cubic Hermite spline curve with the method of speed profile computation is used to determine the trajectory. This method is summarized and extended to allow the optimization with the described constraints. It ensures fulfilment of required initial parameters of motion. The parameters...
Increasing traffic congestion is a major problem in urban areas, which incurs heavy economic and environmental costs in both developing and developed countries. Efficient urban traffic control (UTC) can help reduce traffic congestion. However, the increasing volume and the dynamic nature of urban traffic pose particular challenges to UTC. Reinforcement Learning (RL) has been shown to be a promising...
This paper presents Q-Learning (QL) algorithm based on optimization method to determine optimal glucose feed flow rate profile for the yeast fermentation process. The optimal profile is able to maximize the yeast concentration at the end of the process, meanwhile to minimize the formation of ethanol during the process. The proposed approach is tested under four case studies, which are different in...
We introduce a new fuzzy relational clustering technique with Local Scaling Parameter Learning (LSPL). The proposed approach learns the underlying cluster dependent dissimilarity measure while finding compact clusters in the given data set. The learned measure is a Gaussian similarity function defined with respect to each cluster that allows to control the scaling of the clusters and thus, improve...
The incremental updating of classifiers implies that their internal parameter values can vary according to incoming data. As a result, in order to achieve high performance, incremental learner systems should not only consider the integration of knowledge from new data, but also maintain an optimum set of parameters. In this paper, we propose an approach for performing incremental learning in an adaptive...
Traditionally, multi-plane Support Vector Machines including twin support vector machine (TWSVM) and least squares twin support vector machine (LSTSVM) essentially fail to fully discover the local geometry inside the samples that may be important for classification performance and only preserve the global data structure. This motivates the rush towards new classifiers that can take advantage of underlying...
Probability Collective (PC) is a methodology for distributed optimization by sampling an explicitly parameterized probability distribution over the space of solutions. This parameterization effectively utilizes granules of probability distributions to construct computational models for solving complex systems-level optimization problems. In this paper we present a study of using this probabilistic...
Modern compilers use machine learning to find from their prior experience useful heuristics for new programs encountered in order to accelerate the optimization process. However, prior experience might not be applicable for outlier programs with unfamiliar code features. This paper presents a Reverse K-nearest neighbor (RKNN) algorithm based approach for outlier detection. The compiler can therefore...
In this paper, we propose a cooperative learning algorithm for Multi-category classification which is decomposed into two sub-optimization problems by using the support vector machine technique. The proposed cooperative learning algorithm consists of two single learning algorithms and each sub-optimization problem is solved by one of them. Unlike the cooperative neural network, the proposed cooperative...
Multiple instance learning (MIL) is a recently researched technique used for learning a target concept in the presence of noise. Previously, a random set framework for multiple instance learning (RSF-MIL) was proposed; however, the proposed optimization strategy did not permit the harmonious optimization of model parameters. A cross entropy, based optimization strategy is proposed. Experimental results...
Appropriate feature selection is a very crucial issue in any machine learning framework, specially in Maximum Entropy (ME). In this paper, the selection of appropriate features for constructing a ME based Named Entity Recognition (NER) system is posed as a multiobjective optimization (MOO) problem. Two classification quality measures, namely recall and precision are simultaneously optimized using...
Recently, distance metric learning has been received an increasing attention and found as a powerful approach for semi-supervised learning tasks. In the last few years, several methods have been proposed for metric learning when must-link and/or cannot-link constraints as supervisory information are available. Although many of these methods learn global Mahalanobis metrics, some recently introduced...
Choosing and tuning an optimization procedure for a given class of nonlinear optimization problems is not an easy task. One way to proceed is to consider this as a tournament, where each procedure will compete in different `disciplines'. Here, disciplines could either be different functions, which we want to optimize, or specific performance measures of the optimization procedure. We would then be...
In order to improve the generalization ability of feed-forward neural networks, a new objective function of learning procedure for training single hidden layer network is proposed. This objective function is composed of two information entropy, one is the cross entropy as the main optimization term and the other is the fuzzy entropy as the regularization term. In this paper, we are fused the concept...
Bagging (Bootstrap Aggregating) has been proved to be a useful, effective and simple ensemble learning methodology. In generic bagging methods, all the classifiers which are trained on the different training datasets created by bootstrap resampling original datasets would be seen as base classifiers and their results would be combined to compute final result. This paper proposed a novel ensemble model...
In the last decade, there has been a growing interest in distance function learning for semi-supervised clustering settings. In addition to the earlier methods that learn Mahalanobis metrics (or equivalently, linear transformations), some nonlinear metric learning methods have also been recently introduced. However, these methods either allow limited choice of distance metrics yielding limited flexibility...
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