Wearables have emerged as a revolutionary technology in many application domains including healthcare and fitness. Machine learning algorithms, which form the core intelligence of wearables, traditionally deduce a com...
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ISBN:
(纸本)9781467390057
Wearables have emerged as a revolutionary technology in many application domains including healthcare and fitness. Machine learning algorithms, which form the core intelligence of wearables, traditionally deduce a computational model from a set of training examples to detect events of interest (e.g. activity type). However, in the dynamic environment in which wearables typically operate in, the accuracy of a computational model drops whenever changes in configuration of the system (such as device type and sensor orientation) occur. Therefore, there is a need to develop systems which can adapt to the new configuration autonomously. In this paper, using transfer learning as an organizing principle, we develop several algorithms for data mapping. The data mapping algorithms employ effective signal similarity methods and are used to adapt the system to the new configuration. We demonstrate the efficacy of the data mapping algorithms using a publicly available dataset on human activity recognition.
Reinforcement learning (RL) is a machine learning method that can learn an optimal strategy for a system without knowing the mathematical model of the system. Many RL algorithms are successfully applied in various fie...
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ISBN:
(纸本)9780780394902
Reinforcement learning (RL) is a machine learning method that can learn an optimal strategy for a system without knowing the mathematical model of the system. Many RL algorithms are successfully applied in various fields. However, each algorithm has its advantages and disadvantages. With the increasing complexity of environments and tasks, it is difficult for a single learning algorithm to cope with complicated learning problems with high performance. This motivated us to combine some learning algorithms to improve the learning quality. This paper proposes a new multiple learning architecture, "Aggregated Multiple Reinforcement learning System (AMRLS)". AMRLS adopts three different learning algorithms to learn individually and then combines their results with aggregation methods. To evaluate its performance, AMRLS is tested on two different environments: a Cart-pole System and a Maze environment. The presented simulation results reveal that aggregation not only provides robustness and fault tolerance ability, but also produces more smooth learning curves and needs fewer learning steps than individual learning algorithms.
Identification of fuzzy rules is an important issue in designing of a fuzzy neural network (FNN). However, there is no systematic design procedure at present. In this paper we present a genetic algorithm (G,4) based l...
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ISBN:
(纸本)9780769529769
Identification of fuzzy rules is an important issue in designing of a fuzzy neural network (FNN). However, there is no systematic design procedure at present. In this paper we present a genetic algorithm (G,4) based learning algorithm to make use of the known member-ship function to identify the fuzzy rules form a large set of all possible rules. The proposed learning algorithm initially considers all possible rules then uses the training data and the fitness function to perform rule selection. The proposed GA based learning algorithm has been tested with two different sets of training data. The results obtained from the experiments are promising and demonstrate that the proposed G,4 based learning algorithm can provide a reliable mechanism for fuzzy rule selection.
Quasi-Additive (QA) algorithms are a kind of online learning algorithms having two parameter vectors: One is an accumulation of input vectors and the other is a weight vector for prediction associated with the former ...
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ISBN:
(纸本)9780780394902
Quasi-Additive (QA) algorithms are a kind of online learning algorithms having two parameter vectors: One is an accumulation of input vectors and the other is a weight vector for prediction associated with the former by a non-linear function. We show that the vectors have a dually-flat structure from the information-geometric point of view, which makes it easier to discuss the convergence properties of the algorithms, as presented here.
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)9781424409907
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 objective indices for mined classification rules and evaluations by a human expert for each rule. To enhance adaptability of rule evaluation models, we introduced a constructive meta-learning system to choose proper learning algorithms. Then, we have done the case study on the meningitis data mining as an actual problem.
This paper should contribute to a structured and theoretical view of the backpropagation algorithm and some of its well-known extensions. Basing on a mathematical investigation of the algorithms conditions for structu...
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ISBN:
(纸本)0780327683;0780327691
This paper should contribute to a structured and theoretical view of the backpropagation algorithm and some of its well-known extensions. Basing on a mathematical investigation of the algorithms conditions for structured improvements and developments of these techniques will be described. The construction of adaptive parameter regulations for learning and momentum rate will follow. These parameter regulations allow the presentation of adaptive learning techniques. It will be shown that off-line versions of these techniques represent minimization methods which are exact in mathematical sense. Under consideration of complexity conditions on-line algorithms will be preferred and described in detail. Finally their numerical behaviour will be investigated and simulation results will be presented in comparison with standard algorithms.
We compare two learning algorithms for generating contextual assumptions in automated assume-guarantee reasoning. The CDNF algorithm implicitly represents contextual assumptions by a conjunction of DNF formulae, while...
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ISBN:
(纸本)9783642165573
We compare two learning algorithms for generating contextual assumptions in automated assume-guarantee reasoning. The CDNF algorithm implicitly represents contextual assumptions by a conjunction of DNF formulae, while the OBDD learning algorithm uses ordered binary decision diagrams as its representation. Using these learning algorithms, the performance of assume-guarantee reasoning is compared with monolithic interpolation-based Model Checking in parametrized hardware test cases.
We apply momentum stochastic parallel gradient descent (MSPGD) and policy gradient algorithms to optimize coherent pulse stacking (CPS), and demonstrate their increased effectiveness compared to traditionally used sto...
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ISBN:
(纸本)9781943580910
We apply momentum stochastic parallel gradient descent (MSPGD) and policy gradient algorithms to optimize coherent pulse stacking (CPS), and demonstrate their increased effectiveness compared to traditionally used stochastic parallel gradient descent (SPGD) algorithm. (c) 2021 TheAuthor(s)
The IEEE 802.22 wireless regional area network (WRAN) is the first worldwide commercial application of cognitive radio (CR) networks in unlicensed television broadcast bands. With the intent of efficiently occupying u...
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ISBN:
(纸本)9781424408146
The IEEE 802.22 wireless regional area network (WRAN) is the first worldwide commercial application of cognitive radio (CR) networks in unlicensed television broadcast bands. With the intent of efficiently occupying under-utilized spectrum, the network must be cognizant of spectrum available for secondary use and vacate channels as primary users are present. According to FCC's recent public notice, WRAN products are anticipated to be available for the market by February, 2009. This paper first presents a generic architecture for WRAN cognitive engines (CE), and details the design of a CE leveraging the radio environment map database and case- and knowledge-based learning algorithms (REM-CKL). Furthermore, the performance of REM-CKL CE has been evaluated under various radio scenarios and compared to search-based optimizers, including a genetic algorithm (GA). The simulated results show that the WRAN CE can make significantly faster adaptations and achieve near-optimal utility by synergistically leveraging REMCKL and a local search (LS). Insights into REM-CKL, GA, and LS CE have been gained through the WRAN CE testbed development and preliminary testing.
We derive upper bounds on the generalization error of a learning algorithm in terms of the mutual information between its input and output. The bounds provide an information-theoretic understanding of generalization i...
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We derive upper bounds on the generalization error of a learning algorithm in terms of the mutual information between its input and output. The bounds provide an information-theoretic understanding of generalization in learning problems, and give theoretical guidelines for striking the right balance between data fit and generalization by controlling the input-output mutual information. We propose a number of methods for this purpose, among which are algorithms that regularize the ERM algorithm with relative entropy or with random noise. Our work extends and leads to nontrivial improvements on the recent results of Russo and Zou.
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