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检索条件"主题词=Inference algorithms"

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Analyzing belief function networks with conditional beliefs

Analyzing belief function networks with conditional beliefs

引用

International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Imen Boukhris Zied Elouedi Salem Benferhat LARODEC Institut Supèrieur de Gestion de Tunis Université de Tunis Tunisia CRIL Faculté Jean Perrin Université d'Artois France

The success of Bayesian networks is due to their capability to simply represent (in)dependence and to be a compact representation of a full joint distribution of the set of random variables involved in the studied system. Since belief function theory is known as a general framework to reason under uncertainty, it is expected that belief function networks with conditional beliefs are a generalization of Bayesian networks. This paper studies different forms of belief function networks. We discuss the ones defined with one conditional for all parents and the ones defined per single parent. In particular, we discuss the case when beliefs are Bayesian situations where a belief function network fails to collapse into a Bayesian network.

关键词： Bayesian methods Joints Uncertainty Knowledge engineering Intelligent systems Reliability inference algorithms

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Space alternating data augmentation: application to finite mixture of Gaussians and speaker recognition

Space alternating data augmentation: application to finite m...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： A. Doucet S. Senecal T. Matsui Engineering Department University of Cambridge Cambridge UK Institute for Statistical Mathematics Tokyo Japan

The SAGE (space-alternating generalized expectation-maximization) algorithm (Celeux, G. et al., 2001) is one of the most elegant and popular extensions of the EM (expectation maximization) algorithm for performing ML (maximum likelihood) or MAP (maximum a posteriori) parameter estimation. This algorithm updates parameter components by subblocks by alternating missing data spaces. Its efficiency has been reported in numerous simulation studies. We propose here an MCMC (Markov chain Monte Carlo) strategy named SADA (space-alternating data augmentation) which relies on the same principle in order to sample efficiently from (posterior) distributions and we discuss its application to finite mixtures of Gaussians. For this model, we also present an original implementation of the SAGE algorithm. In Monte Carlo simulations and in an application for speaker recognition, these methods, which are straightforward modifications of the standard EM and DA (data augmentation) algorithms, consistently outperform them.

关键词： Gaussian processes Speaker recognition Maximum likelihood estimation inference algorithms Convergence Sampling methods Parameter estimation Monte Carlo methods Gaussian distribution Bayesian methods

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FUN: optimization of fuzzy rule based systems using neural networks

FUN: optimization of fuzzy rule based systems using neural n...

引用

International Conference on Neural Networks

作者： S.M. Sulzberger N. Tschichold-Gurman S.J. Vestli Institute of Robotics ETH Zurich Zurich Switzerland

A method for optimization of fuzzy rule based systems using neural networks is described. A neural network model with special neurons has been developed so that the translation of fuzzy rules and membership functions into the network is possible. The performance of this network, and hence the quality of the original rule base, is then improved by training the network using a combination of neural network learning algorithms. The optimized rules and membership functions can be extracted from the net and used in normal fuzzy inference tools. This net has been tested on the WallJumperOver and the problem of local navigation for mobile robots.< >

关键词： Fuzzy neural networks Fuzzy systems Knowledge based systems Neural networks Optimization methods Neurons inference algorithms Testing Navigation Mobile robots

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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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Incremental DCOP Search algorithms for Solving Dynamic DCOP Problems

Incremental DCOP Search Algorithms for Solving Dynamic DCOP ...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： William Yeoh Pradeep Varakantham Xiaoxun Sun Sven Koenig Department of Computer Science New Mexico State University School of Information Systems Singapore Management University Google Inc Mountain View CA US Department of Computer Science University of Southern California

ISBN: (纸本)9781467396196

Distributed constraint optimization (DCOP) problems are well-suited for modeling multi-agent coordination problems. However, it only models static problems, which do not change over time. Consequently, researchers have introduced the Dynamic DCOP (DDCOP) model to model dynamic problems. In this paper, we make two key contributions: (a) a procedure to reason with the incremental changes in DDCOP problems and (b) an incremental pseudo-tree construction algorithm that can be used by DCOP algorithms such as any-space ADOPT and any-space BnB-ADOPT to solve DDCOP problems. Due to the incremental reasoning employed, our experimental results show that any-space ADOPT and any-space BnB-ADOPT are up to 42% and 38% faster, respectively, with the incremental procedure and the incremental pseudo-tree reconstruction algorithm than without them.

关键词： Search problems Heuristic algorithms Upper bound Computational modeling Context inference algorithms Computer science

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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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Hidden mode HMM using Bayesian network for modeling speaking rate fluctuation

Hidden mode HMM using Bayesian network for modeling speaking...

引用

IEEE Workshop on Automatic Speech Recognition and Understanding

作者： T. Shinozaki S. Furui Department of Computer Science Tokyo Institute of Technology Japan

One of the most important issues in spontaneous speech recognition is how to cope with the degradation of recognition accuracy due to speaking rate fluctuation within an utterance. This paper proposes an acoustic model for adjusting mixture weights and transition probabilities of the HMM for each frame according to the local speaking rate. The proposed model is implemented along with variants and conventional models using the Bayesian network framework. The proposed model has a hidden variable representing variation of the "mode" of the speaking rate and its value controls the parameters of the underlying HMM. Model training and maximum probability assignment of the variables are conducted using the EM/GEM and inference algorithms for Bayesian networks. Utterances from meetings and lectures are used for evaluation where Bayesian network-based acoustic models are used to rescore the utterance hypotheses obtained from a first-pass N-best list. In the experiments, the proposed model shows consistently higher performance than conventional models.

关键词： Hidden Markov models Bayesian methods Fluctuations Speech recognition Computer science Degradation inference algorithms Text recognition Acoustic testing Decoding

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On Geometric Structure of Quasi-Additive Learning algorithms

On Geometric Structure of Quasi-Additive Learning Algorithms

引用

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...

引用

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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Electronic Kanji Dictionary Based on "Dasher"

Electronic Kanji Dictionary Based on "Dasher"

引用

IEEE Mountain Workshop on Adaptive and Learning Systems

作者： Chris Winstead Electrical and Computer Engineering Utah State University Logan UT USA

ISBN: (纸本)1424401666

A new interface is proposed for efficiently searching ideographic character sets such as the Japanese Kanji alphabet. The proposed interface is based on MacKay's Dasher, an interface originally designed to enable high-speed computer input for disabled persons. The Dasher interface allows a user to form words and sentences by "gliding" through a field of characters. This paper considers adapting the Dasher approach for gliding through a complex and unfamiliar character space. Information-theoretic algorithms are proposed to make the interface tolerant to variations among individuals' interpretations of Kanji characters, so that it is immediately usable by persons with no prior knowledge of Kanji. A software concept demonstration of the proposed dictionary is available, implementing some of the proposed features and algorithms

关键词： Dictionaries Natural languages Computer interfaces Software libraries Books Mice Writing Software algorithms Asia inference algorithms

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