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检索条件"主题词=inference algorithms"

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Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets

Learning for Structured Prediction Using Approximate Subgrad...

引用

IEEE Conference on Computer Vision and Pattern Recognition

作者： Aurelien Lucchi Yunpeng Li Pascal Fua Computer Vision Laboratory EPFL

ISBN: (纸本)9781467364102

We propose a working set based approximate subgradient descent algorithm to minimize the margin-sensitive hinge loss arising from the soft constraints in max-margin learning frameworks, such as the structured SVM. We focus on the setting of general graphical models, such as loopy MRFs and CRFs commonly used in image segmentation, where exact inference is intractable and the most violated constraints can only be approximated, voiding the optimality guarantees of the structured SVM's cutting plane algorithm as well as reducing the robustness of existing sub gradient based methods. We show that the proposed method obtains better approximate subgradients through the use of working sets, leading to improved convergence properties and increased reliability. Furthermore, our method allows new constraints to be randomly sampled instead of computed using the more expensive approximate inference techniques such as belief propagation and graph cuts, which can be used to reduce learning time at only a small cost of performance. We demonstrate the strength of our method empirically on the segmentation of a new publicly available electron microscopy dataset as well as the popular MSRC data set and show state-of-the-art results.

关键词： Labeling Fasteners inference algorithms Approximation algorithms Training Image segmentation Convergence

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On Geometric Structure of Quasi-Additive Learning algorithms

On Geometric Structure of Quasi-Additive Learning Algorithms

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International Joint Conference on Neural Networks (IJCNN)

作者： K. Ikeda Department of Systems Science Kyoto University Kyoto Japan

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.

关键词： Information geometry Convergence Neural networks Nonlinear equations inference algorithms Information analysis Algorithm design and analysis Information theory Mathematical programming Physics

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Sound Event Detection on GradCAM method with Complex Pretrained Model

Sound Event Detection on GradCAM method with Complex Pretr...

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Computer Graphics, Image and Virtualization (ICCGIV), International Conference on

作者： Xichang Cai Ziling Qiao Yanggang Gan Juan Wu Menglong Wu Hongxia Dong Information College North China University of Technology Beijing

GradCAM weakly supervised algorithm is usually used in classification tasks as a feature visualization method. This method can mark the pixel points determined by CNN as classification features by the target layer parameters of convolutional neural network. Based on the ability of GradCAM to locate classification features, this paper combines the audio event detection task with GradCAM algorithm to form an audio event detection system based on Grad*** feature localization ability obtained by convolutional neural network in the audio event classification task is used to assist in completing the audio event detection task. In the experiment based on DCASE2022task4 dataset, the PSDS2 score of 0.74157 is higher than the baseline system with PSDS2 score of 0.54336.

关键词： Training Visualization Event detection Semantic segmentation Feature extraction inference algorithms Classification algorithms

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A Novel Dynamic Spectrum Access Strategy Applied to Cognitive Radio Network

A Novel Dynamic Spectrum Access Strategy Applied to Cognitiv...

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International Conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Xuchen Liu Wenzhu Zhang

This paper proposes a dynamic spectrum access (DSA) strategy in cognitive radio (CR) network. This strategy combines fuzzy inference and reinforcement learning theory to build knowledge on network status from the current state and past experience. Multiple assessment variables on environment performance and preference information are introduced to regulate decision-making behavior. The proposed aims at making optimal spectrum access action and obtaining long-term rewards on desired network-wide objectives. We adopt distribute DSA architecture in which cognitive radio network access points (AP) executive control strategy and provide CR network access for unlicensed users. Numerical results show that our algorithm can dramatically promote efficient use of spectrum resource compared to other spectrum access methods, and gain good rewards on the desirable objectives.

关键词： Cognitive radio Decision making Learning Educational institutions inference algorithms Convergence Interference

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An interval-valued fuzzy reasoning algorithm based on OWA operator

An interval-valued fuzzy reasoning algorithm based on OWA op...

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IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS)

作者： Qiansheng Zhang Shengyi Jiang Shihua Luo Haixiang Yao School of Information Science and Technology Guangdong University of Foreign Studies (GDUFS) Guangzhou China School of Information Management Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9781424447374;9781424447541

This paper presents a weighted interval-valued fuzzy (IvF) reasoning algorithm based on OWA operator for IvF inference system where interval-valued fuzzy set (IvFS) theory and IvF production rules with parameters are used for uncertain knowledge representation. The proposed algorithm can perform IvF matching between the fact and the antecedent portion of IvF rule by employing the weighted similarity measure. Finally, one numeric example illustrates that the proposed method is very flexible and simple.

关键词： Fuzzy reasoning Open wireless architecture In vitro fertilization inference algorithms Fuzzy set theory Fuzzy systems Production systems Knowledge representation Performance evaluation Weight measurement

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A new sequential Monte Carlo algorithm for distributed blind equalization

A new sequential Monte Carlo algorithm for distributed blind...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Claudio J. Bordin Marcelo G. S. Bruno Instituto Tecnológico de Aeronáutica Sao Jose dos Campos Brazil

This article describes a new distributed blind equalization algorithm based on particle filters suitable for CDMA systems operating on frequency-selective channels. The proposed approach processes the signal received by multiple receivers in a non-centralized fashion, determining approximately optimal estimates (MAP) of the transmitted symbols, which minimizes the expected error rate. The described receiver structure dispenses with the need of a data fusion center, and is capable of operating either independently or in cooperation with other receivers. As we verified via numerical simulations, cooperative operation leads to a significant performance improvement, without substantially increasing the algorithmpsilas computational complexity.

关键词： Receivers Blind equalizers Particle filters Signal processing algorithms Approximation methods inference algorithms Multiaccess communication

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A multivariable fuzzy logic controller (MFLC) for a buck DC-DC converter

A multivariable fuzzy logic controller (MFLC) for a buck DC-...

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Annual IEEE Conference on Power Electronics Specialists (PESC)

作者： J.A. Asumadu E. Ho Electrical and Computer Engineering Department Western Michigan University USA

This paper describes the design and construction of a multivariable fuzzy controller (MFLC) for the control of a DC-DC buck converter. An off-the-shelf hardware-based MFLC system has been developed that is used to model the variable switching structure of the buck converter. The MFLC design criterion is based on the error in output voltage and the change of error in the output voltage as inputs of the controller, and the changes in the duty cycle ratio as output of the controller. The error and change of error are two membership functions bounded in the interval [0, 1]. The two inputs have two fuzzy subsets, negative fuzzy subset N and positive fuzzy subset P. The output is made up of fuzzy subsets of singletons. It will be shown that when the error and change of error have the same signs, the output moves away from a set point and therefore the same reasoning or rules can be used to change the output. The results will show that the MFLC technique provides robust control for nonlinear power electronics variable switching structure like the DC-DC buck converter.

关键词： Fuzzy logic DC-DC power converters Electric variables control Buck converters Sliding mode control Error correction Voltage control Power electronics Circuit topology inference algorithms

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Lightning Fast Matching Dependency Discovery with Desbordante

Lightning Fast Matching Dependency Discovery with Desbordant...

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Open Innovations Association (FRUCT), Conference of

作者： Alexey Shlyonskikh Michael Sinelnikov Daniil Nikolaev Yurii Litvinov George Chernishev Saint-Petersburg University Saint-Petersburg Russia

ISBN: (数字)9789526524627

ISBN: (纸本)9798331507411

Matching dependency is a generalization of the functional dependency concept, which allows users to apply custom similarity functions for matching individual attributes. Matching dependencies have a wide range of applications for solving various data quality problems, such as entity resolution, data deduplication, data integration, schema matching, and many more. However, their discovery is a very computationally intensive problem, which limits their practical *** this paper, we describe a number of optimization techniques for HyMD — currently the state-of-the-art algorithm for the discovery of matching dependencies. These optimizations belong to both technical and scientific domains. The most important of them are: 1) a new sampling technique, 2) a faster generalization lookup technique, and 3) an improved representation of a dependency. The first one aims to raise the efficiency of inference from record pairs, while the last two are designed to speed up lattice-related *** evaluate our optimizations, we implemented our version of HyMD in Desbordante, an open-source high-performance data profiler. Experiments demonstrated that they allow for a speedup of more than 40x over the state-of-the-art implementation on average, reaching a speedup greater than 170x in some ***, the improved version of HyMD is ready to use by anyone. It comes with bidirectional Python integration, which allows calling the C++ algorithm implementation from Python programs while allowing users to supply their custom matching functions.

关键词： Technological innovation Data integrity Memory management Lightning Data integration inference algorithms Maintenance Optimization Pattern matching Python

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On the use of recurrent neuro-fuzzy networks for predictive control

On the use of recurrent neuro-fuzzy networks for predictive ...

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： A.R. Sadeghian J.D. Lavers Department of Math Physics & Computer Science Ryerson University Toronto ONT Canada Department of Electrical & Computer Engineering University of Toronto Toronto ONT Canada

This paper presents the application of recurrent neuro-fuzzy networks for the predictive control of nonlinear, multivariable, complex systems such as electric arc furnaces. The main objectives are to investigate the capability of adaptive neuro-fuzzy networks to predict the V-I characteristics of electric arc furnaces and to compare the performance of the proposed predictors with that of the feedforward neuro-fuzzy predictors. The novelties of this work are to propose the notion of approximate prediction and to implement it using a recurrent neuro-fuzzy structure suitable for long-term prediction. Successful implementations of recurrent neuro-fuzzy predictors are described and their performances are illustrated.

关键词： Fuzzy neural networks Predictive control Furnaces Adaptive systems inference algorithms Voltage fluctuations Neural networks Steel Recurrent neural networks Fuzzy logic

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Unsupervised energy disaggregation with factorial hidden Markov models based on generalized backfitting algorithm

Unsupervised energy disaggregation with factorial hidden Mar...

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IEEE Region 10 International Conference TENCON

作者： Lanruo Wang Xianjue Luo Wei Zhang School of Electrical Engineering Xi'an Jiaotong University Xi'an China

This paper proposes an approximate inference and parameter estimation in factorial hidden Markov models (FHMMs), a generalization of hidden Markov models (HMMs) in which the state is factored into multiple Markov chains. Earlier research has proved exact inference and parameter estimation can be computationally intractable. The proposed method in this paper considers FHMMs to be an alternative generalization of generalized additive models (GAMs). We use backfitting algorithm to estimate the parameters in FHMMs instead of exact but complex derivation and make the approximate inference more efficient. This method is motivated by the problem of energy disaggregation which is the process of decomposing a whole household's electricity consumption into individual appliances in order to improve energy utility efficiency both in load terminal and electricity supplier. Numerical simulations indicate the effectiveness of the proposed method for energy disaggregation.

关键词： Home appliances Hidden Markov models Markov processes Parameter estimation Approximation algorithms inference algorithms Additives

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