检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

286 篇 会议
134 篇 期刊文献

馆藏范围

420 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

288 篇 工学
- 211 篇 计算机科学与技术...
- 191 篇 软件工程
- 74 篇 信息与通信工程
- 25 篇 机械工程
- 24 篇 生物工程
- 20 篇 电气工程
- 19 篇 电子科学与技术（可...
- 18 篇 控制科学与工程
- 13 篇 光学工程
- 10 篇 化学工程与技术
- 9 篇 生物医学工程（可授...
- 5 篇 仪器科学与技术
- 3 篇 动力工程及工程热...
- 3 篇 交通运输工程
- 2 篇 材料科学与工程（可...
178 篇 理学
- 119 篇 物理学
- 71 篇 数学
- 40 篇 统计学（可授理学、...
- 29 篇 生物学
- 20 篇 系统科学
- 11 篇 化学
40 篇 管理学
- 27 篇 图书情报与档案管...
- 12 篇 管理科学与工程(可...
- 5 篇 工商管理
8 篇 法学
- 7 篇 社会学
5 篇 文学
- 3 篇 外国语言文学
- 2 篇 中国语言文学
- 2 篇 新闻传播学
4 篇 医学
- 4 篇 临床医学
2 篇 经济学
- 2 篇 应用经济学
2 篇 农学
1 篇 艺术学

主题

70 篇 speech recogniti...
41 篇 hidden markov mo...
40 篇 training
30 篇 neural machine t...
23 篇 machine translat...
20 篇 decoding
19 篇 handwriting reco...
18 篇 computer aided l...
17 篇 recurrent neural...
15 篇 feature extracti...
15 篇 transducers
14 篇 vocabulary
12 篇 databases
12 篇 error analysis
11 篇 speech
10 篇 pattern recognit...
10 篇 humans
9 篇 training data
9 篇 optimization
9 篇 context

机构

52 篇 human language t...
40 篇 human language t...
37 篇 apptek gmbh aach...
32 篇 human language t...
31 篇 human language t...
23 篇 apptek gmbh aach...
20 篇 human language t...
16 篇 human language t...
13 篇 human language t...
12 篇 pattern recognit...
10 篇 human language t...
9 篇 spoken language ...
9 篇 human language t...
8 篇 computer science...
8 篇 human language t...
7 篇 pattern recognit...
6 篇 pattern recognit...
6 篇 human language t...
6 篇 human language t...
6 篇 human language t...

作者

210 篇 ney hermann
80 篇 hermann ney
61 篇 schlüter ralf
22 篇 ralf schluter
21 篇 ralf schlüter
19 篇 wuebker joern
18 篇 casacuberta fran...
18 篇 zeyer albert
17 篇 zhou wei
16 篇 gao yingbo
14 篇 kim yunsu
14 篇 herold christian
13 篇 mansour saab
13 篇 zeineldeen moham...
13 篇 patrick doetsch
12 篇 peris álvaro
12 篇 peter jan-thorst...
12 篇 michel wilfried
12 篇 peitz stephan
12 篇 huck matthias

语言

417 篇 英文
1 篇 西班牙文
1 篇 其他
1 篇 中文

检索条件"机构=Pattern Recognition and Human Language Technology Center"

共 420 条记录，以下是261-270 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

The rwth aachen german-english machine translation system for WMT 2014 9

The rwth aachen german-english machine translation system fo...

引用

9th Workshop on Statistical Machine Translation, WMT 2014 at the 52nd Conference of the Associationfor Computational Linguistics, ACL 2014

作者： Peitz, Stephan Wuebker, Joern Freitag, Markus Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781941643174

This paper describes the statistical machine translation (SMT) systems developed at RWTH Aachen University for the German!English translation task of the ACL 2014 Eighth Workshop on Statistical Machine Translation (WMT 2014). Both hierarchical and phrase-based SMT systems are applied employing hierarchical phrase reordering and word class language models. For the phrase-based system, we run discriminative phrase training. In addition, we describe our preprocessing pipeline for German!English. © Proceedings of the Annual Meeting of the Association for Computational Linguistics 2014.

关键词： Computer aided language translation

来源：评论

学校读者我要写书评

暂无评论

RWTHLM - The RWTH Aachen University neural network language modeling toolkit 15

RWTHLM - The RWTH Aachen University neural network language ...

引用

15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken languages, INTERSPEECH 2014

作者： Sundermeyer, Martin Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Spoken Language Processing Group LIMSI CNRS Paris France

We present a novel toolkit that implements the long short-term memory (LSTM) neural network concept for language modeling. The main goal is to provide a software which is easy to use, and which allows fast training of standard recurrent and LSTM neural network language models. The toolkit obtains state-of-the-art performance on the standard Treebank corpus. To reduce the training time, BLAS and related libraries are supported, and it is possible to evaluate multiple word sequences in parallel. In addition, arbitrary word classes can be used to speed up the computation in case of large vocabulary sizes. Finally, the software allows easy integration with SRILM, and it supports direct decoding and rescoring of HTK lattices. The toolkit is available for download under an open source license. Copyright © 2014 ISCA.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Vector space models for phrase-based machine translation 8

Vector space models for phrase-based machine translation

引用

8th Workshop on Syntax, Semantics and Structure in Statistical Translation, SSST 2014

作者： Alkhouli, Tamer Guta, Andreas Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany Spoken Language Processing Group Univ. Paris-Sud France LIMSI/CNRS Orsay France

ISBN: (纸本)9781937284961

This paper investigates the application of vector space models (VSMs) to the standard phrase-based machine translation pipeline. VSMs are models based on continuous word representations embedded in a vector space. We exploit word vectors to augment the phrase table with new inferred phrase pairs. This helps reduce out-of-vocabulary (OOV) words. In addition, we present a simple way to learn bilingually-constrained phrase vectors. The phrase vectors are then used to provide additional scoring of phrase pairs, which fits into the standard log-linear framework of phrase-based statistical machine translation. Both methods result in significant improvements over a competitive in-domain baseline applied to the Arabic-to-English task of IWSLT 2013. © 2014 Association for Computational Linguistics

关键词： Vectors

来源：评论

学校读者我要写书评

暂无评论

Translation modeling with bidirectional recurrent neural networks

Translation modeling with bidirectional recurrent neural net...

引用

2014 Conference on Empirical Methods in Natural language Processing, EMNLP 2014

作者： Sundermeyer, Martin Alkhouli, Tamer Wuebker, Joern Ney, Hermann Human Language Technology Pattern Recognition Group RWTH Aachen University Aachen Germany Spoken Language Processing Group Univ. Paris-Sud France LIMSI/CNRS Orsay France

ISBN: (纸本)9781937284961

This work presents two different translation models using recurrent neural networks. The first one is a word-based approach using word alignments. Second, we present phrase-based translation models that are more consistent with phrasebased decoding. Moreover, we introduce bidirectional recurrent neural models to the problem of machine translation, allowing us to use the full source sentence in our models, which is also of theoretical interest. We demonstrate that our translation models are capable of improving strong baselines already including recurrent neural language models on three tasks: IWSLT 2013 German!English, BOLT Arabic!English and Chinese!English. We obtain gains up to 1.6% BLEU and 1.7% TER by rescoring 1000-best lists. © 2014 Association for Computational Linguistics.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Cipher type detection

Cipher type detection

引用

2014 Conference on Empirical Methods in Natural language Processing, EMNLP 2014

作者： Nuhn, Malte Knight, Kevin Computer Science Department Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany Information Sciences Institute University of Southern California United States

ISBN: (纸本)9781937284961

Manual analysis and decryption of enciphered documents is a tedious and error prone work. Often-even after spending large amounts of time on a particular cipher-no decipherment can be found. Automating the decryption of various types of ciphers makes it possible to sift through the large number of encrypted messages found in libraries and archives, and to focus human effort only on a small but potentially interesting subset of them. In this work, we train a classifier that is able to predict which encipherment method has been used to generate a given ciphertext. We are able to distinguish 50 different cipher types (specified by the American Cryptogram Association) with an accuracy of 58.5%. This is a 11.2% absolute improvement over the best previously published classifier. © 2014 Association for Computational Linguistics.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

The RWTH Large Vocabulary Arabic Handwriting recognition System

The RWTH Large Vocabulary Arabic Handwriting Recognition Sys...

引用

IAPR International Workshop on Document Analysis Systems, DAS

作者： Mahdi Hamdani Patrick Doetsch Michal Kozielski Amr El-Desoky Mousa Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

This paper describes the RWTH system for large vocabulary Arabic handwriting recognition. The recognizer is based on Hidden Markov Models (HMMs) with state of the art methods for visual/language modeling and decoding. The feature extraction is based on Recurrent Neural Networks (RNNs) which estimate the posterior distribution over the character labels for each observation. Discriminative training using the Minimum Phone Error (MPE) criterion is used to train the HMMs. The recognition is done with the help of n-gram language Models (LMs) trained using in-domain text data. Unsupervised writer adaptation is also performed using the Constrained Maximum Likelihood Linear Regression (CMLLR) feature adaptation. The RWTH Arabic handwriting recognition system gave competitive results in previous handwriting recognition competitions. The used techniques allows to improve the performance of the system participating in the OpenHaRT 2013 evaluation.

关键词： Hidden Markov models Handwriting recognition Feature extraction Training Vocabulary Speech recognition Context

来源：评论

学校读者我要写书评

暂无评论

Towards Unsupervised Learning for Handwriting recognition

Towards Unsupervised Learning for Handwriting Recognition

引用

International Workshop on Frontiers in Handwriting recognition

作者： Michal Kozielski Malte Nuhn Patrick Doetsch Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

We present a method for training an off-line handwriting recognition system in an unsupervised manner. For an isolated word recognition task, we are able to bootstrap the system without any annotated data. We then retrain the system using the best hypothesis from a previous recognition pass in an iterative fashion. Our approach relies only on a prior language model and does not depend on an explicit segmentation of words into characters. The resulting system shows a promising performance on a standard dataset in comparison to a system trained in a supervised fashion for the same amount of training data.

关键词： Hidden Markov models Training Vocabulary Error analysis Handwriting recognition Training data Ciphers

来源：评论

学校读者我要写书评

暂无评论

Fine-Grained Visual Categorization with 2D-Warping

Fine-Grained Visual Categorization with 2D-Warping

引用

International Conference on pattern recognition

作者： Harald Hanselmann Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

The task of fine-grained visual categorization is related to both general object recognition and specialized tasks such as face recognition. Hence, we propose to combine two methods popular for general object recognition and face recognition to build a new model-free system for fine-grained visual categorization. Specifically, we use Local Naive-Bayes Nearest Neighbor as a pre-selection method and 2D-Warping as a refinement step. For the latter, we explore different ways to use the alignments computed by a 2D-Warping algorithm for classification. We demonstrate the performance of our approach on the CUB200-2011 database and show that our approach outperforms the recognition accuracy of current state-of-the-art methods.

关键词： Training Accuracy Visualization Face recognition Databases Birds Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Multilingual Off-Line Handwriting recognition in Real-World Images

Multilingual Off-Line Handwriting Recognition in Real-World ...

引用

IAPR International Workshop on Document Analysis Systems, DAS

作者： Michal Kozielski Patrick Doetsch Mahdi Hamdani Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

We propose a state-of-the-art system for recognizing real-world handwritten images exposing a huge degree of noise and a high out-of-vocabulary rate. We describe methods for successful image demising, line removal, deskewing, deslanting, and text line segmentation. We demonstrate how to use a HMM-based recognition system to obtain competitive results, and how to further improve it using LSTM neural networks in the tandem approach. The final system outperforms other approaches on a new dataset for English and French handwriting. The presented framework scales well across other standard datasets.

关键词： Hidden Markov models Handwriting recognition Standards Training Image segmentation Vocabulary Algorithm design and analysis

来源：评论

学校读者我要写书评

暂无评论

ICFHR2014 Competition on Handwritten Text recognition on Transcriptorium Datasets (HTRtS)

ICFHR2014 Competition on Handwritten Text Recognition on Tra...

引用

International Workshop on Frontiers in Handwriting recognition

作者： Joan Andreu Sánchez Verónica Romero Alejandro H. Toselli Enrique Vidal Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

A contest on Handwritten Text recognition organised in the context of the ICFHR 2014 conference is described. Two tracks with increased freedom on the use of training data were proposed and three research groups participated in these two tracks. The handwritten images for this contest were drawn from an English data set which is currently being considered in the Tran scriptorium project. The goal of this project is to develop innovative, efficient and cost-effective solutions for the transcription of historical handwritten document images, focusing on four languages: English, Spanish, German and Dutch. For the English language, the so-called "Bentham collection" is being considered in Tran scriptorium. It encompasses a large set of manuscripts written by the renowned English philosopher and reformer Jeremy Bentham (1748-1832). A small subset of this collection has been chosen for the present HTR competition. The selected subset has been written by several hands (Bentham himself and his secretaries) and entails significant variabilities and difficulties regarding the quality of text images and writing styles. Training and test data were provided in the form of carefully segmented line images, along with the corresponding transcripts. The three participants achieved very good results, with transcription word error rates ranging from 15.0% down to 8.6%.

关键词： Training Hidden Markov models Histograms Artificial neural networks Text recognition Training data Adaptive optics

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共42页 << < 23 24 25 26 27 28 29 30 31 32 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：