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Spectrum sharing technologies are a kind of important technologies to overcome the shortage of the wireless spectrum, among which the centralized spectrum sharing exhibits superior performance in dense region. However, this method does suffer from large computational complexity and impractical implementation when optimization target is and overall system is complex. In this paper, reinforcement learning...
Ant colony optimization (ACO) is an evolutionary computing approach for combinatorial optimization problems. Recently, some extensions of ACO have been proposed in continuous domains. However, these methods did not consider the dependency between variables and thus may fail for some complex optimization problems. In this paper, we use Bayesian factorizations to capture the main dependency of the variables...
Modern multicore architectures require runtime optimization techniques to address the problem of mismatches between the dynamic resource requirements of different processes and the runtime allocation. Choosing between multiple optimizations at runtime is complex due to the non-additive effects, making the adaptiveness of the machine learning techniques useful. We present a novel method, Machine Learned...
Defining a suitable metric is one of the biggest challenges in deformable image fusion from different modalities. In this paper, we propose a novel approach for multi-modal metric learning in the deformable registration framework that consists of embedding data from both modalities into a common metric space whose metric is used to parametrize the similarity. Specifically, we use image representation...
Applications have emerged in the last years in which several dissimilarities and data sources provide complementary information about the problem. Therefore, metric learning algorithms should be developed that integrate all this information in order to reflect better which is similar for the user and the problem at hand. In this paper, we propose a semi-supervised algorithm to learn a linear combination...
Multi-modality, the unique and important property of video data, is typically ignored in existing video adaptation processes. To solve this problem, we propose a novel approach, named multi-modality transfer based on multi- graph optimization (MMT-MGO) in this paper, which leverages multi-modality knowledge generalized by auxiliary classifiers in the source domain to assist multi-graph optimization...
Learning a compact and yet discriminative codebook for classifying human actions is a challenging problem. One difficulty lies in that the learning procedure is split into two independent phases (dimension reduction and clustering) and thus results in the loss of discriminative information which clustering requires. Besides, traditional used principal component analysis is not optimized for class...
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...
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...
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...
This paper combines artificial immune and emotional learning methods to solve the path optimization problems in complex, dynamic and real-time multi-agent systems. In artificial immune algorithm, path metric is defined as the affinity function between antigen and antibody, namely, the matching degree between optimal path and candidate paths. At the same time, emotional learning method is used to train...
Traditional k-NN classifier poses many limitations including that it does not take into account each class distribution, importance of each feature, contribution of each neighbor, and the number of instances for each class. A Differential evolution (DE) optimization technique is utilized to enhance the performance of k-NN through optimizing the metric weights of features, neighbors and classes. Several...
Constrained clustering (semi-supervised learning) techniques have attracted more attention in recent years. However, the commonly used constraints are restricted to the instance level, thus we introduced two new classifications for the type of constraints: decision constraints and non-decision constraints. We implemented applications involving non-decision constraints to find alternative clusterings...
MDQL is an algorithm, based on reinforcement learning, for solving multiple objective optimization problems, that has been tested on several applications with promising results. MDQL discretizes the decision variables into a set of states, each associated with actions to move agents to contiguous states. A group of agents explore this state space and are able to find Pareto sets applying a distributed...
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