检索结果-内蒙古大学图书馆

Dissociable effects of methylphenidate, atomoxetine and placebo on regional cerebral blood flow in healthy volunteers at rest: A multi-class pattern recognition approach

引用

NEUROIMAGE 2012年第2期60卷 1015-1024页

作者： Marquand, Andre F. O'Daly, Owen G. De Simoni, Sara Alsop, David C. Maguire, R. Paul Williams, Steven C. R. Zelaya, Fernando O. Mehta, Mitul A. Kings Coll London Dept Neuroimaging Ctr Neuroimaging Sci Inst Psychiat London SE5 8AF England Beth Israel Deaconess Med Ctr Boston MA 02215 USA Harvard Univ Sch Med Boston MA USA Pfizer Global Res & Dev Groton CT USA

The stimulant drug methylphenidate (MPH) and the non-stimulant drug atomoxetine (ATX) are both widely used for the treatment of attention deficit/hyperactivity disorder (ADHD), but their differential effects on human brain function are poorly understood. PET and blood oxygen level dependent (BOLD) fMRI have been used to study the effects of MPH and BOLD fMRI is beginning to be used to delineate the effects of MPH and ATX in the context of cognitive tasks. The BOLD signal is a proxy for neuronal activity and is dependent on three physiological parameters: regional cerebral blood flow (rCBF), cerebral metabolic rate of oxygen and cerebral blood volume. To identify areas sensitive to MPH and ATX and assist interpretation of BOLD studies in healthy volunteers and ADHD patients, it is therefore of interest to characterize the effects of these drugs on rCBF. In this study, we used arterial spin labeling (ASL) MRI to measure rCBF non-invasively in healthy volunteers after administration of MPH, ATX or placebo. We employed multi-class pattern recognition (PR) to discriminate the neuronal effects of the drugs, which accurately discriminated all drug conditions from one another and provided activity patterns that precisely localized discriminating brain regions. We showed common and differential effects in cortical and subcortical brain regions. The clearest differential effects were observed in four regions: (i) in the caudate body where MPH but not ATX increased rCBF, (ii) in the midbrain/substantia nigra and (iii) thalamus where MPH increased and ATX decreased rCBF plus (iv) a large region of cerebellar cortex where ATX increased rCBF relative to MPH. Our results demonstrate that combining ASL and PR yields a sensitive method for detecting the effects of these drugs and provides insights into the regional distribution of brain networks potentially modulated by these compounds. (c) 2012 Elsevier Inc. All rights reserved.

关键词： Methylphenidate Atomoxetine Arterial spin labeling multi-class pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Sphere-structured support vector machines for multi-class pattern recognition 9th

引用

9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Grandular Computing (RSFDGrC 2003)

作者： Zhu, ML Wang, Y Chen, SF Liu, XD Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Peoples R China

ISBN: (纸本)3540140409

Support vector machines (SVM) are learning algorithms derived from statistical learning theory. The SVM approach was originally developed for binary classification problems. For solving multi-class classification problem, there are some methods such as one-against-rest, one-against-one, altogether and so on. But the computing time of all these methods are too long to solve large scale problem. In this paper SVMs architectures for multi-class problems are discussed, in particular we provide a new algorithm called sphere-structured SVMs to solve the multi-class problem. We show the algorithm in detail and analyze its characteristics. Not only the number of convex quadratic programming problems in sphere-structured SVMs is small, but also the number of variables in each programming is least. The computing time of classification is reduced. Otherwise, the characteristics of sphere-structured SVMs make expand data easily.

关键词： support vector machines multi-class pattern recognition sphere-structured kernel function quadratic programming

来源：评论

学校读者我要写书评

暂无评论

Binary Neural Networks for classification of Voice Commands From Throat Microphone

引用

IEEE ACCESS 2018年 6卷 70130-70144页

作者： Ribeiro, Fabio Cisne Santos Carvalho, Raphael Torres Cortez, Paulo Cesar De Albuquerque, Victor Hugo C. Reboucas Filho, Pedro Pedrosa Univ Fed Ceara Dept Teleinformat Engn Pici Campus BR-60440970 Fortaleza Ceara Brazil Fed Inst Educ Sci & Technol Ceara BR-63475000 Jaguaribe Brazil Univ Fortaleza Grad Program Appl Informat BR-60811905 Fortaleza Ceara Brazil Fed Inst Educ Sci & Technol Ceara BR-61939140 Maracanau Brazil

multi-class pattern classification has many applications including speech recognition, and it is not easy to extend from two-class neural networks (NNs). This paper presents a study about using binary classifiers with NNs together with a perceptual linear prediction (PLP) method for feature extraction to increase the classification rate of voice commands captured using a throat microphone, comparing this method with a single NN. Because there is no other data set with voice commands captured using a throat microphone in the Brazilian Portuguese language in researched literature, we created a data set with isolated voice commands with utterances captured from 150 people (men and women). All the voice samples are captured in Brazilian Portuguese, and they are the digits "0" through "9" and the words "Ok" and "Cancel". The results show that the throat microphone is robust in noise environment, achieving 95.4% of hit rate in our speech recognition system with multiple NNs using the one-against-all approach, better performance than a simple NN that reach 91.88%. This result is very representative, since both classifiers obtained high hit rates. But, it requires 535% more time for training the multiple NNs compared with simple NN. The best configuration on PLP extraction order is 9 or 10 for voice samples captured by the throat microphone, which was observed that poor stressed vowel and fricative-like words "3" and "7" in Portuguese confuses the classifier.

关键词： multi-class pattern recognition speech recognition neural networks binary classifiers

来源：评论

学校读者我要写书评

暂无评论

New Combined S-transform and Logistic Model Tree Technique for recognition and classification of Power Quality Disturbances

引用

ELECTRIC POWER COMPONENTS AND SYSTEMS 2011年第1期39卷 80-98页

作者： Moravej, Z. Abdoos, A. A. Pazoki, M. Semnan Univ Dept Elect & Comp Engn Fac Elect & Comp Engn Semnan 13955 Iran

This article presents certain features and a new classification method for recognition of power quality disturbances. The proposed method uses S-transform to extract the most severable features of the power quality waveforms. The disturbances decision-making scheme is performed using the logistic model tree. This new algorithm requires fewer features compared to the wavelet based approach for the identification of power quality events. Thus, the required memory space and learning logistic model tree time are substantially reduced. The most common types of disturbances, including sag, swell, interruption, harmonics, transient, and flicker, are studied. Disturbances consisting of both sag and harmonics, as well as both swell and harmonics, are also considered. The result shows that the classifier can detect and classify different power quality events correctly. Sensitivity of the proposed algorithm under noisy condition is also investigated.

关键词： power quality disturbances feature extraction multi-class pattern recognition logistic model tree S-transform

来源：评论

学校读者我要写书评

暂无评论

Wavelet transform and multi-class relevance vector machines based recognition and classification of power quality disturbances

引用

EUROPEAN TRANSACTIONS ON ELECTRICAL POWER 2011年第1期21卷 212-222页

作者： Moravej, Zahra Pazoki, Mohammad Abdoos, Ali Akbar Semnan Univ Fac Elect & Comp Engn Semnan Iran

recognition of power quality events by analyzing the voltage waveform disturbances is a very important task for the power system monitoring. This paper presents an integrated model for recognizing power quality disturbances (PQDs) using a new wavelet multi-class relevance vector machines (WMRVMs). The so-called relevance vector machines (RVMs) is an effective classification tool. The proposed method employs the wavelet transform techniques to extract the most important and significant feature from details and approximation waves. The obtained feature vectors are used for training the RVMs to classify the PQDs. Various transient events are tested, the result shows that the classifier can detect and classify different power quality signal correctly. Finally, sensitivity of the proposed algorithm under noise condition is investigated in this paper. Copyright (C) 2010 John Wiley & Sons, Ltd.

关键词： feature extraction multi-class pattern recognition power quality disturbances relevance vector machines (RVMs) wavelet transform

来源：评论

学校读者我要写书评

暂无评论

Hybrid signal processing and machine intelligence techniques for detection, quantification and classification of power quality disturbances

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2009年第3期22卷 442-454页

作者： Panigrahi, B. K. Dash, P. K. Reddy, J. B. V. Coll Engn Ctr Res Elect Elect & Comp Engn Bhubaneswar 751023 Orissa India Indian Inst Technol Dept Elect Engn Delhi India Dept Sci & Technol New Delhi India

This paper presents an advanced signal processing technique known as S-transform (ST) to detect and quantify various power quality (PQ) disturbances. ST is also utilized to extract some useful features of the disturbance signal. The excellent time-frequency resolution characteristic of the ST makes it an attractive candidate for analysis of power system disturbance signals. The number of features required in the proposed approach is less than that of the wavelet transform (WT) for identification of PQ disturbances. The features extracted by using ST are used to train a support vector machine (SVM) classifier for automatic classification of the PQ disturbances. Since the proposed methodology can reduce the features of disturbance signal to a great extent without losing its original property, it efficiently utilizes the memory space and computation time of the processor. Eleven types of PQ disturbances are considered for the classification purpose. The simulation results show that the combination of ST and SVM can effectively detect and classify different PQ disturbances. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Power quality disturbances S-transform Wavelet transform Feature extraction Support vector machine multi-class pattern recognition

来源：评论

学校读者我要写书评

暂无评论

multilogistic regression by evolutionary neural network as a classification tool to discriminate highly overlapping signals:: Qualitative investigation of volatile organic compounds in polluted waters by using headspace-mass spectrometric analysis

引用

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2008年第2期92卷 179-185页

作者： Hervas, Cesar Silva, Manuel Gutierrez, Pedro Antonio Serrano, Antonio Univ Cordoba Dept Analyt Chem E-14071 Cordoba Spain Univ Cordoba Dept Comp Sci E-14071 Cordoba Spain

This work investigates the ability of multilogistic regression models including nonlinear effects of the covariates as a multi-class pattern recognition technique to discriminate highly overlapping analytical signals using a very short number of input covariates. For this purpose, three methodologies recently reported by us were applied based on the combination of linear and nonlinear terms which are transformations of the linear ones by using evolutionary product unit neural networks. To test this approach, drinking water samples contaminated with volatile organic compounds such as benzene, toluene, xylene and their mixtures were classified in seven classes through the very close data provided by their headspace-mass spectrometric analysis. Instead of using the total ion current profile provided by the MS detector as input covariates, the three-parameter Gaussian curve associated to it was used as linear covariates for the standard multilogistic regression model, whereas the product unit basic functions or their combination with the linear covariates were used for the nonlinear models, The hybrid nonlinear model, pruned by a backward stepwise method, provided the best classification results with a correctly classified rate for the training and generalization sets of 100% and 76.2%, respectively. The reduced dimensions of the proposed model: only three terms, namely one initial covariate and two basis product units, enabled to infer interesting interpretations from a chemical point of view. (C) 2008 Elsevier B.V. All rights reserved.

关键词： logistic regression product unit neural networks multi-class pattern recognition volatile organic compounds headspace-mass spectrometric analysis

来源：评论

学校读者我要写书评

暂无评论

Fast and Robust Vision System for Shogi Robot

引用

JOURNAL OF ROBOTICS AND MECHATRONICS 2015年第2期27卷 182-190页

作者： Koutaki, Gou Uchimura, Keiichi Kumamoto Univ Prior Org Innovat & Excellence 2-39-1 Kurokami Kumamoto 8608555 Japan Kumamoto Univ Grad Sch Sci & Technol Kumamoto 8608555 Japan

The authors developed a low-cost, safety shogi robot system. A Web camera installed on the lower frame is used to recognize pieces and their positions on the board, after which the game program is played. A robot arm moves a selected piece to the position used in playing a human player. A fast, robust image processing algorithm is needed because a low-cost wide-angle Web camera and robot are used. The authors describe image processing and robot systems, then discuss experiments conducted to verify the feasibility of the proposal, showing that even a low-cost system can be highly reliable.

关键词： shogi robot arm picking multi-class pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Windowed State Space Filters for Peak Interference Suppression in Neural Spike Sorting 30

Windowed State Space Filters for Peak Interference Suppressi...

引用

30th European Signal Processing Conference (EUSIPCO)

作者： Baeriswyl, Christof Bertrand, Alexander Wildhaber, Reto A. Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS Ctr Dynam Syst Signal Proc & Data Analyt Leuven Belgium Bern Univ Appl Sci Biel Switzerland Katholieke Univ Leuven Inst Artificial Intelligence Leuven AI Leuven Belgium Univ Appl Sci & Arts Northwestern Switzerland Windisch Switzerland Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9789082797091

Signal-to-peak-interference ratio (SPIR) optimal filters are template matching filters with peak interference suppression properties. Such max-SPIR filters are used in multi-pattern recognition problems, such as neural spike sorting in micro-electrode array probes, where cellular action potentials need to be detected and clustered according to their firing neuron cells. In high-density probes with hundreds of channels, such max-SPIR filter banks can require unacceptable high computational resources, in particular for applications with real-time demands and/or on-probe spike sorting. In this paper, we present a computationally attractive substitute for max-SPIR filters by recursively computed Autonomous Linear State Space Model (ALSSM) filters. In our approach, we approximate the impulse response of max-SPIR filters by low order ALSSMs and perform the signal convolution in the new, low-dimensional ALSSM vector space. We demonstrate our method on real neural recordings from high-density probes and show only minimal loss in detection quality while the computational complexity drops by up to a factor 10.

关键词： multi-class pattern recognition template matching linear state space models neural spike sorting

来源：评论

学校读者我要写书评

暂无评论

A new multi-class support vector machine with multi-sphere in the feature space

引用

20th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

作者： Hao, Pei-Yi Lin, Yen-Hsiu Natl Kaohsiung Univ Appl Sci Dept Informat Management Kaohsiung Taiwan Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan 701 Taiwan

ISBN: (纸本)9783540733225

Support vector machine (SVM) is a very promising classification technique developed by Vapnik. However, there are still some shortcomings in the original SVM approach. First, SVM was originally designed for binary classification. How to extend it effectively for multi-class classification is still an on-going research issue. Second, SVM does not consider the distribution of each class. In this paper, we propose an extension to the SVM method of pattern recognition for solving the multi-class problem in one formal step. Contrast to previous multi-class SVMs, our approach considers the distribution of each class. Experimental results show that the proposed method is more suitable for practical use than other multi-class SVMs, especially for unbalanced datasets.

关键词： support vector machine SVM multi-class pattern recognition kernel-based learning one-class SVM

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：