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Online reinforcement learning for continuous-state systems 专著章节/文集论文
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Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, 2014
作者:  Yuanheng Zhu;  Zhao DB(赵冬斌)
Adobe PDF(24150Kb)  |  收藏  |  浏览/下载：268/30  |  提交时间：2017/09/13
基于深度神经网络的语音识别技术及应用 学位论文
, 中国科学院自动化研究所: 中国科学院大学, 2014
作者:  范利春
Adobe PDF(4420Kb)  |  收藏  |  浏览/下载：754/0  |  提交时间：2015/09/02
大词汇量连续语音识别  深度神经网络  鲁棒语音特征  局部连接的深度神经网络  多语言混合识别  关键词检测  Large Vocabulary Continue Speech Recognition (Lvcsr)  Deep Neural Network (Dnn)  Robust Speech Feature  Partially Connected Dnn  Mixed-language Speech Recognition  Spoken Term Detection (Std)  
Rear-View Vehicle Detection and Tracking by Combining Multiple Parts for Complex Urban Surveillance 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2014, 卷号: 15, 期号: 2, 页码: 597-606
作者:  Tian, Bin;  Li, Ye;  Li, Bo;  Wen, Ding;  Bin Tian
Adobe PDF(1776Kb)  |  收藏  |  浏览/下载：408/165  |  提交时间：2015/08/12
Kalman Filter (Kf)  Markov Random Field (Mrf)  Part-based Object Detection  Tracking  Vehicle Detection  
Hierarchical sparse representation based Multi-Instance Semi-Supervised Learning with application to image categorization 期刊论文
SIGNAL PROCESSING, 2014, 卷号: 94, 页码: 595-607
作者:  Feng, Songhe;  Xiong, Weihua;  Li, Bing;  Lang, Congyan;  Huang, Xiankai
Adobe PDF(5203Kb)  |  收藏  |  浏览/下载：257/77  |  提交时间：2015/08/12
Multi-instance Semi-supervised Learning  Hierarchical Sparse Representation  Weighted Multi-instance Kernel  Image Categorization  
Unsupervised language model adaptation for handwritten Chinese text recognition 期刊论文
PATTERN RECOGNITION, 2014, 卷号: 47, 期号: 3, 页码: 1202-1216
作者:  Wang, Qiu-Feng;  Yin, Fei;  Liu, Cheng-Lin
浏览  |  Adobe PDF(3154Kb)  |  收藏  |  浏览/下载：309/57  |  提交时间：2015/08/12
Character String Recognition  Chinese Handwriting Recognition  Unsupervised Language Model Adaptation  Language Model Compression  
Minimum-risk training for semi-Markov conditional random fields with application to handwritten Chinese/Japanese text recognition 期刊论文
PATTERN RECOGNITION, 2014, 卷号: 47, 期号: 5, 页码: 1904-1916
作者:  Zhou, Xiang-Dong;  Zhang, Yan-Ming;  Tian, Feng;  Wang, Hong-An;  Liu, Cheng-Lin
浏览  |  Adobe PDF(751Kb)  |  收藏  |  浏览/下载：313/84  |  提交时间：2015/08/12
Semi-markov Conditional Random Fields  Minimum-risk Training  Character String Recognition  
Character confidence based on N-best list for keyword spotting in online Chinese handwritten documents 期刊论文
PATTERN RECOGNITION, 2014, 卷号: 47, 期号: 5, 页码: 1880-1890
作者:  Zhang, Heng;  Wang, Da-Han;  Liu, Cheng-Lin
浏览  |  Adobe PDF(3340Kb)  |  收藏  |  浏览/下载：344/90  |  提交时间：2015/08/12
Online Chinese Handwritten Documents  Keyword Spotting  Posterior Probability  N-best List  Confidence Measure  Confusion Network  
A graph matching algorithm based on concavely regularized convex relaxation 期刊论文
NEUROCOMPUTING, 2014, 卷号: 134, 页码: 140-148
作者:  Liu, Zhi-Yong;  Qiao, Hong;  Jia, Li-Hao;  Xu, Lei
Adobe PDF(1370Kb)  |  收藏  |  浏览/下载：204/4  |  提交时间：2015/08/12
Graph Matching  Concave-convex Procedure  Concave Regularization  Frank-wolfe Algorithm  Convex Relaxation  
Globality and locality incorporation in distance metric learning 期刊论文
NEUROCOMPUTING, 2014, 卷号: 129, 期号: 1, 页码: 185-198
作者:  Wang, Wei;  Hu, Bao-Gang;  Wang, Zeng-Fu
浏览  |  Adobe PDF(5033Kb)  |  收藏  |  浏览/下载：286/93  |  提交时间：2015/08/12
Mahalanobis Distance  Metric Learning  Convex Optimization  Classification