We present two online gradient learning algorithms to design condensed k-nearest neighbor (NN) classifiers. The goal of these learning procedures is to minimize a measure of performance closely related to the expected...
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ISBN:
(纸本)3540660690
We present two online gradient learning algorithms to design condensed k-nearest neighbor (NN) classifiers. The goal of these learning procedures is to minimize a measure of performance closely related to the expected misclassification rate of the k-NN classifier. One possible implementation of the algorithm is given. Converge properties are analyzed and connections with other works are established. We compare these learning procedures with Kononen's LVQ algorithms [7] and k-NN classification using the handwritten NIST databases [5]. Experimental results demonstrate the potential of the proposed learning algorithms.
In this paper, we critically analyse the performance of an intelligent Long-Term Evolution-Uplink (LTE-UL) system having a cognitive engine (CE) embedded in e-NodeB. Performance characterization, optimal radio paramet...
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ISBN:
(纸本)9781479980888
In this paper, we critically analyse the performance of an intelligent Long-Term Evolution-Uplink (LTE-UL) system having a cognitive engine (CE) embedded in e-NodeB. Performance characterization, optimal radio parameters prediction, and inter-cell-interference coordination (ICIC) are studied. The embedded CE allocates the optimal radio parameters to serving users and suggests the acceptable transmit power to users served by adjacent cells for ICIC. The desired cognition has been achieved with a novel random neural network (RNN) based CE architecture. To achieve the best learning performance, we critically analysed three learning algorithms, gradient descent (GD), adaptive inertia weight particle swarm optimization (AIW-PSO) and differential evolution (DE). The analysis showed that AIW-PSO was 10.57% better than GD and 8.012% better than DE in terms of learning accuracy (based on MSE), but with considerable compromise on computational time as compared to GD. Moreover, in terms of convergence time (to achieve the MSE less than 1.04E-03), AIW-PSO took 60% less iterations than GD and 50% less than DE.
The work presented in this paper provides a practical, customized learning algorithm for reinforcement learning tasks that evolve episodically over acyclic state spaces. The presented results are motivated by the Opti...
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ISBN:
(纸本)9781424445783
The work presented in this paper provides a practical, customized learning algorithm for reinforcement learning tasks that evolve episodically over acyclic state spaces. The presented results are motivated by the Optimal Disassembly Planning (ODP) problem described in [14], and they complement and enhance some earlier developments on this problem that were presented in [15]. In particular, the proposed algorithm is shown to be a substantial improvement of the original algorithm developed in [15], in terms of, both, the involved computational effort and the attained performance, where the latter is measured by the accumulated reward. The new algorithm also leads to a robust performance gain over the typical Q-learning implementations for the considered problem context.
In this paper, we present an evaluation of learning algorithms of a novel rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indices. Post-processing of ...
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ISBN:
(纸本)354045764X
In this paper, we present an evaluation of learning algorithms of a novel rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indices. Post-processing of mined results is one of the key processes in a data mining process. However, it is difficult for human experts to completely evaluate several thousands of rules from a large dataset with noises. To reduce the costs in such rule evaluation task, we have developed the rule evaluation support method with rule evaluation models which learn from a dataset. This dataset comprises objective indices for mined classification rules and evaluations by a human expert for each rule. To evaluate performances of learning algorithms for constructing the rule evaluation models, we have done a case study on the meningitis data mining as an actual problem. Furthermore, we have also evaluated our method with five rule sets obtained from five UCI datasets.
This thesis models online ensemble learning algorithms to obtain theoretical analyses of various performance metrics. Online ensemble learning algorithms often serve to learn unknown, possibly time-varying, probabili...
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This thesis models online ensemble learning algorithms to obtain theoretical analyses of various performance metrics. Online ensemble learning algorithms often serve to learn unknown, possibly time-varying, probability distributions or interact with other learning systems. Their simplicity allows flexibility in design choices, leading to variations that balance adaptiveness and consistency and allows for chatter resistant co-learning. To analyze online ensemble learning algorithms for these variations this work provides a method for the creation of automata by properly selecting states. These automata provide an analytical framework to quantify the adaptiveness and consistency of online ensemble learning algorithms when interacting with a probability distribution. The resulting Markov chain provides quantatative metrics of adaptiveness and consistency can be calculated through mathematical formulas, other than relying on numerical simulations. This analysis shows that the Multi Expert Algorithm (MEA) achieves a higher consistency than the more adaptive Weighted Majority Algorithm (WMA), and a higher adaptiveness than the more consistent Winnow algorithm, thus achieving a balance between the historical algorithms in terms of the adaptiveness and consistency metrics. The automata also provides an analytical framework to identify chatter which can happen when an online learning algorithm is used by a robot to predict human intention when interacting with a human. When chatter happens, the learning algorithm continually changes its prediction, without reaching a constant prediction of human intention. Utilizing Rescorla-Wagner model for human learning, we analyze an expert based online learning algorithm and identify if chatter will occur, and if so what conditions will cause chatter.
The Internet of Things (IoT) is a network of interconnected devices that may be used to remotely detect, identify, and operate physical objects. IoT's qualities allow for the incorporation of the real world into a...
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This paper proposes a practical list of safety concerns and mitigation methods for visual deep learning algorithms. The growing success of deep learning algorithms in solving nonlinear and complex problems has recentl...
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The research on traditional lithium battery charging systems has problems such as model simplification, insufficient data, insufficient accuracy, and poor real-time performance. Simplified electrochemical models canno...
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Our research proposes a comprehensive approach to identify duplicate frames in digital videos. It integrates machine learning and signal processing techniques for effective identification. The process begins with pre-...
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The idea of two-phase learning has been proposed here for effectively solving the learning problems in which training instances come in a two-stage way. Several two-phase learning algorithms based on the learning meth...
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The idea of two-phase learning has been proposed here for effectively solving the learning problems in which training instances come in a two-stage way. Several two-phase learning algorithms based on the learning method PRISM have also been proposed for inducing rules from training instances. These alternatives form a spectrum, showing achievement of the requirement of PRISM being heavily dependent on the spent computational cost in Phase 2.
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