检索结果-内蒙古大学图书馆

Tree-structured Bayesian network learning with application to scene classification

ELECTRONICS LETTERS 2011年第9期47卷 540-541页

作者： Wang, Z. F. Wang, Z. H. Xie, W. J. Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China

A tree-structured Bayesian network is one of the best probabilistic models for scene classification. A simple and successful learning algorithm for a tree-structured Bayesian network classifier is presented without taking arc reversal into account. A systematic performance study on the Lazebnik 15 dataset shows the proposed method is more efficient than the traditional learning scheme.

关键词： Optical, image and video signal processing learning (artificial intelligence) File organisation scene classification belief networks Lazebnik dataset Knowledge engineering techniques tree data structures tree-structured Bayesian network classifier learning algorithm image classification Computer vision and image processing techniques probabilistic models

来源：评论

学校读者我要写书评

暂无评论

Low-dimensional-Structure Self-learning and Thresholding: Regularization Beyond Compressed Sensing for MRI Reconstruction

引用

MAGNETIC RESONANCE IN MEDICINE 2011年第3期66卷 756-767页

作者： Akcakaya, Mehmet Basha, Tamer A. Goddu, Beth Goepfert, Lois A. Kissinger, Kraig V. Tarokh, Vahid Manning, Warren J. Nezafat, Reza Beth Israel Deaconess Med Ctr Dept Med Boston MA 02215 USA Harvard Univ Sch Med Boston MA USA Beth Israel Deaconess Med Ctr Dept Radiol Boston MA 02215 USA Harvard Univ Sch Engn & Appl Sci Cambridge MA 02138 USA

An improved image reconstruction method from undersampled k-space data, low-dimensional-structure self-learning and thresholding (LOST), which utilizes the structure from the underlying image is presented. A low-resolution image from the fully sampled k-space center is reconstructed to learn image patches of similar anatomical characteristics. These patches are arranged into 'similarity clusters,' which are subsequently processed for dealiasing and artifact removal, using underlying low-dimensional properties. The efficacy of the proposed method in scan time reduction was assessed in a pilot coronary MRI study. Initially, in a retrospective study on 10 healthy adult subjects, we evaluated retrospective undersampling and reconstruction using LOST, wavelet-based I-1-norm minimization, and total variation compressed sensing. Quantitative measures of vessel sharpness and mean square error, and qualitative image scores were used to compare reconstruction for rates of 2,3, and 4. Subsequently, in a prospective study, coronary MRI data were acquired using these rates, and LOST-reconstructed images were compared with an accelerated data acquisition using uniform undersampling and sensitivity encoding reconstruction. Subjective image quality and sharpness data indicate that LOST outperforms the alternative techniques for all rates. The prospective LOST yields images with superior quality compared with sensitivity encoding or I-1-minimization compressed sensing. The proposed LOST technique greatly improves image reconstruction for accelerated coronary MRI acquisitions. Magn Reson Med 66:756-767,2011. (C)2011 Wiley-Liss, Inc.

关键词： compressed sensing accelerated imaging image reconstruction block matching learning algorithm cardiac MR coronary MRI

来源：评论

学校读者我要写书评

暂无评论

Appliance Usage Prediction Using a Time Series Based Classification Approach

Appliance Usage Prediction Using a Time Series Based Classif...

引用

Annual Conference on IEEE Industrial Electronics Society

作者： Kaustav Basu Vincent Debusschere Seddik Bacha Grenoble Electrical Engineering Laboratory (G2E lab)

ISBN: (纸本)9781467324199

Energy management for residential homes and offices require the prediction of the usage(s) or service request(s) of different appliances present in the house. The hardware requirement is more simplified and practical if the task is only based on energy consumption data and no other sensors are used. The proposed model tries to formalize such an approach using a time-series based multi-label classifier which takes into account correlation between different appliances among other factors. In this work, prediction results are shown for 1-hour in the future but this approach can be extended to predict more hours in the future as per the requirement (with restrictions). The learned models and decision tree showing the important factors in the input information is also discussed.

关键词： Appliance Usage Prediction learning algorithm Energy Management in Homes Data Mining Smart-Buildings Multi-label classifiers Decision tree

来源：评论

学校读者我要写书评

暂无评论

learning of Audiovisual Integration

Learning of Audiovisual Integration

引用

IEEE International Conference on Development and learning (ICDL)

作者： Yan, Rujiao Rodemann, Tobias Wrede, Britta Univ Bielefeld Res Inst Cognit & Robot CoR Lab D-33594 Bielefeld Germany Honda Res Inst Europe GmbH D-63073 Offenbach Germany

ISBN: (纸本)9781612849904

We present a system for learning audiovisual integration based on temporal and spatial coincidence. The current sound is sometimes related to a visual signal that has not yet been seen, we consider this situation as well. Our learning algorithm is tested in online adaptation of audio-motor maps. Since audio-motor maps are not reliable at the beginning of the experiment, learning is bootstrapped using temporal coincidence when there is only one auditory and one visual stimulus. In the course of time, the system can automatically decide to use both spatial and temporal coincidence depending on the quality of maps and the number of visual sources. We can show that this audiovisual integration can work when more than one visual source appears. The integration performance does not decrease when the related visual source has not yet been spotted. The experiment is executed on a humanoid robot head.

关键词： Robot kinematics Tracking audio signal processing audio-motor maps audio-visual integration learning bootstrapped learning humanoid robot head humanoid robots learning algorithm robot vision spatial coincidence statistical analysis temporal coincidence visual signal

来源：评论

学校读者我要写书评

暂无评论

Tissue classification in magnetic resonance images through the hybrid approach of Michigan and Pittsburg genetic algorithm

引用

APPLIED SOFT COMPUTING 2011年第4期11卷 3476-3484页

作者： Mehta, Shashi Bhushan Chaudhury, Santanu Bhattacharyya, Asok Jena, Amarnath Delhi Coll Engn Delhi 110042 India Indian Inst Technol Delhi 110016 India Rajiv Gandhi Canc Inst & Res Ctr MRI Ctr Chief MRI Delhi 110085 India

Magnetic resonance system generates image data, where the contrast is dependent on various parameters like proton density (PD), spin lattice relaxation time (T1), spin-spin relaxation time (T2), chemical shift, flow effect, diffusion, and perfusion. There is a lot of variability in the intensity pattern in the magnetic resonance (MR) image data due to various reasons. For example a T2 weighted image of same patient can be generated by different pulse sequence (Spin Echo, Fast Spin Echo, Inversion recovery, etc.) or on different MR system (1T, 1.5T, 3T, system, etc.) or using different RF coil system. Hence, there is a need for an adaptive scheme for segmentation, which can be modified depending on the imaging scheme and nature of the MR images. This paper proposes a scheme to automatically generate fuzzy rules for MR image segmentation to classify tissue. The scheme is based on hybrid approach of two popular genetic algorithm based machine learning (GBML) techniques, Michigan and Pittsburg approach. The proposed method uses a training data set generated from manual segmented images with the help of an expert in magnetic resonance imaging (MRI). Features from image histogram and spatial neighbourhood of pixels have been used in fuzzy rules. The method is tested for classifying brain T2 weighted 2-D axial images acquired by different pulse sequences into three primary tissue types: white matter (WM), gray matter (GM), and cerebro spinal fluid (CSF). Results were matched with manual segmentation by experts. The performance of our scheme was comparable. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Segmentation Pattern analysis Fuzzy rules FRBS learning algorithm Pittsburg Michigan Genetic algorithm Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

A Neural Architecture to Address Reinforcement learning Problems

A Neural Architecture to Address Reinforcement Learning Prob...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： de Arruda, Rodrigo L. S. Von Zuben, Fernando J. Univ Campinas UNICAMP Sch Elect & Comp Engn FEEC Dept Comp Engn & Ind Automat DCA Lab Bioinformat & Bioinspired Comp LBiC Campinas SP Brazil

ISBN: (纸本)9781424496365

In this paper, the Reinforcement learning problem is formulated equivalently to a Markov Decision Process. We address the solution of such problem using a novel Adaptive Dynamic Programming algorithm which is based on a Multi-layer Perceptron Neural Network composed of a parameterized function approximator called Wire-Fitting. Extending such established model, this work makes use of concepts of eligibility to conceive faster learning algorithms. The advantage of the proposed approach is founded on the capability to handle continuous environments and to learn a better policy while following another. Simulation results involving the automatic control of an inverted pendulum are presented to indicate the effectiveness of the proposed algorithm.

关键词： Approximation methods Dynamic programming Equations Heuristic algorithms Markov decision process Markov processes Markov processes Mathematical model Monte Carlo methods adaptive dynamic programming automatic control dynamic programming inverted pendulum learning (artificial intelligence) learning algorithm multilayer perceptron neural network multilayer perceptrons neural architecture neural net architecture parameterized function approximator reinforcement learning wire-fitting

来源：评论

学校读者我要写书评

暂无评论

Temporal Difference learning with Interpolated N-Tuples: Initial Results from a Simulated Car Racing Environment

Temporal Difference Learning with Interpolated N-Tuples: Ini...

引用

IEEE Conference on Computational Intelligence and Games (CIG)

作者： Abdullahi, Aisha A. Lucas, Simon M. Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

ISBN: (纸本)9781457700095

Evolutionary algorithms have been used successfully in car racing game competitions, such as the ones based on TORCS. This is in contrast to temporal difference learning (TDL), which despite being a powerful learning algorithm, has not been used to any significant extent within these competitions. We believe that this is mainly due to the difficulty of choosing a good function approximator, the potential instability of the learning behavior (and hence the reliability of the results), and the lack of a forward model which restricts the choice of TDL algorithms. This paper reports our initial results on using a new type of function approximator designed to be used with TDL for problems with a large number of continuous-valued inputs, where function approximators such as multi-layer perceptrons can be unstable. The approach combines interpolated tables with n-tuple systems. In order to conduct the research in a flexible and efficient way we developed a new car-racing simulator that runs much more quickly than TORCS and gives us full access to the forward model of the system. We investigate different types of tracks and physics models, and also make comparisons with human drivers and some initial tests with evolutionary learning (EL). The results show that each approach leads to different driving styles, and either TDL or EL can learn best depending on the details of the environment. Significantly, TDL produced best results when learning state-action values (similar to Q-learning;no forward model needed). Regarding driving style, TDL consistently learned behaviours that avoid damage while EL tended to evolve fast but reckless drivers.

关键词： Computational intelligence Computational modeling Function approximation Games Sensors Software TORCS car racing game competition car racing simulator continuous-valued inputs evolutionary algorithm evolutionary computation evolutionary learning forward model function approximator interpolated n-tuples learning (artificial intelligence) learning algorithm learning behavior multilayer perceptron reckless drivers simulated car racing environment state-action values temporal difference learning traffic engineering computing

来源：评论

学校读者我要写书评

暂无评论

A Model of Neuronal Intrinsic Plasticity

引用

IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT 2011年第4期3卷 277-284页

作者： Li, Chunguang Zhejiang Univ Dept Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

Recent experimental results have accumulated evidence that the neurons can change their response characteristics to adapt to the variations of the synaptic inputs, which is the so-called neuronal intrinsic plasticity mechanism. In this paper, we present a new model on neuronal intrinsic plasticity. We first show that the probability distribution of the neuronal firing rates is more suitable to be represented as a Weibull distribution than an exponential distribution. Then, we derive the intrinsic plasticity model based on information theory. This study provides a more realistic model for further research on the effects of intrinsic plasticity on various brain functions and dynamics.

关键词： Information theory intrinsic plasticity learning algorithm model Weibull distribution

来源：评论

学校读者我要写书评

暂无评论

Simulation and evaluation of dual fully fuzzy linear systems by fuzzy neural network

引用

APPLIED MATHEMATICAL MODELLING 2011年第10期35卷 5026-5039页

作者： Otadi, Mahmood Mosleh, Maryam Islamic Azad Univ Dept Math Firoozkooh Branch Firoozkooh Iran

In this paper, a novel hybrid method based on fuzzy neural network for approximate solution of fuzzy linear systems of the form Ax = Bx + d, where A and 8 are two square matrices of fuzzy coefficients, x and d are two fuzzy number vectors, is presented. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate solution, a simple and fast algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Neural networks Dual linear system learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Fuzzy polynomial regression with fuzzy neural networks

引用

APPLIED MATHEMATICAL MODELLING 2011年第11期35卷 5400-5412页

作者： Mosleh, M. Otadi, M. Abbasbandy, S. Islamic Azad Univ Sci & Res Branch Dept Math Tehran Iran Islamic Azad Univ Firoozkooh Branch Dept Math Firoozkooh Iran

Recently, fuzzy linear regression is considered by Mosleh et al. [1]. In this paper, a novel hybrid method based on fuzzy neural network for approximate fuzzy coefficients (parameters) of fuzzy polynomial regression models with fuzzy output and crisp inputs, is presented. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Neural network Fuzzy polynomial regression model learning algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：