检索结果-内蒙古大学图书馆

It's about time: Signal recognition in staged models of protein translocation

pattern recognition 2009年第4期42卷 567-574页

作者： Buske, Fabian A. Maetschke, stefan Boden, Mikael Univ Queensland Inst Mol Biosci Brisbane Qld 4072 Australia

During their synthesis, a large fraction of proteins are directed to the secretory pathway. There are several models that aim to distinguish between different destinations along this pathway;however, they rarely distinguish between known stages of this translocation Process. This paper presents a translocation probability function which models the protein SRP-recruitment process-the first stage of the secretory pathway. It unifies groups of proteins with distinct final destinations, allowing more specific sorting to be done in due course, mirroring the hierarchical nature of secretory translocation. We apply conditional random fields to evaluate the prediction accuracy of a full sequence model. Introducing the translocation function improves Substantially compared to a model based on properties that are relevant to the subsequent stages and final destinations only. For the discrimination of secretory, signal peptide (SP)-equipped proteins and non-secretory proteins a correlation coefficient of 0.98 is achieved-a level of performance that is only met by specialized SP predictors. Transmembrane proteins cause considerable confusion in signal peptide predictors, but fit naturally into our transparent design and reduce the performance of the translocation function only slightly. The proposed function and model assist efforts to uncover localization and function for the growing numbers of protein sequence data. Applying our model we estimate with high confidence that about 27% of the human and 29% of the mouse proteins are associated with the secretory pathway. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Bioinformatics machine learning Protein secretory pathway Signal peptide Conditional random field Amino acid sequence

来源：评论

学校读者我要写书评

暂无评论

learning Dynamic Bayesian Network structure from Non-time Symmetric data

Learning Dynamic Bayesian Network Structure from Non-time Sy...

引用

Chinese Conference on pattern recognition/1st CJK Joint workshop on pattern recognition

作者： Wang Shuang-cheng Shao Jun Cheng Xin-zhang Shanghai Lixin Univ Commerce Sch Math & Informat Shanghai 201620 Peoples R China Shanghai Lixin Univ Commerce Sch Accounting & Finance Shanghai 201620 Peoples R China Shanghai Lixin Univ Commerce Open Econ & Tracle Res Ctr Shanghai 201620 Peoples R China

ISBN: (纸本)9781424441990

At present, there are not the methods of learning dynamic Bayesian network structure from no time symmetry data. In this paper, a method of learning dynamic Bayesian network structure from non-time symmetric data is developed by dint of transfer variables. In this method, first transfer variables between two adjacent time slices are learned by combining star structure and Gibbs sampling. Then dynamic Bayesian network part structure can be built based on sorting nodes and local search & scoring method. A complete dynamic Bayesian network structure can be obtained by extending along time series.

关键词： dynamic Bayesian network non-time symmetric data transfer variable structure learning

来源：评论

学校读者我要写书评

暂无评论

Feature Selection for Classifying data stream Based on Maximum Entropy

Feature Selection for Classifying Data Stream Based on Maxim...

引用

Chinese Conference on pattern recognition/1st CJK Joint workshop on pattern recognition

作者： Liu, Yao-zong Wang, Yong-li Wei, Wei Zhang, Hong Nanjing Univ Sci & Technol Sch Comp Nanjing 210094 Peoples R China

ISBN: (纸本)9781424441990

Feature select ion is an important problem in the fields of machine learning and pat tern recognition. data stream data classification with high dimensional and sparse, and the dimension of the need for compression, feature selection methods suitable for data stream classification study of very value of this area is currently a lack of in-depth study. This paper summarizes the current data flow classification feature selection research, analysis of the characteristics of different methods. Based on the principle of maximum entropy, naive Bayes with the technology on the data stream tuple feature selection attributes, divided into two different subsets of the merits, so as to enhance the work of C4.5 classifier results, the experiment proved not only strearnMEFS classification of time-saving, but also to improve the quality of the classification.

关键词： Feature Selection data stream Information Gain Maximum Entropy

来源：评论

学校读者我要写书评

暂无评论

Using Boosting and Clustering to Prune Bagging and Detect Noisy data

Using Boosting and Clustering to Prune Bagging and Detect No...

引用

Chinese Conference on pattern recognition/1st CJK Joint workshop on pattern recognition

作者： Xie, Yuan-Cheng Yang, Jing-Yu Nanjing Univ Sci & Technol Sch Comp Sci & Technol Nanjing 210094 Peoples R China

ISBN: (纸本)9781424441990

AdaBoost has been the representation of ensemble learning algorithm because of its excellent performance. However, due to its longtime training, AdaBoost was complained about by people and this defect limits the practical application. Bagging is a rapid method of training and supports for parallel computing. One of important factors that can affect the performance of ensemble learning is the diversity of component learners. Based on this view, a new algorithm using clustering and Boosting to prune Bagging ensembles is proposed in this paper. Its learning efficiency is close to Bagging and its performance is close to AdaBoost. Furthermore, this new algorithm can detect noisy data from original samples based on cascade technique, and a better result of noise detection can be acquired.

关键词： Boosting Clustering Selective ensemble Cascade BP neural network Detection of noisy data

来源：评论

学校读者我要写书评

暂无评论

Acquiring background knowledge for intelligent tutoring systems

Acquiring background knowledge for intelligent tutoring syst...

引用

1st international Conference on Educational data mining, EDM 2008

作者： Antunes, Cláudia Dep of Computer Science and Engineering Instituto Superior Técnico Av. Rovisco Pais 1 1049-001 Lisboa Portugal

ISBN: (纸本)9780615306292

One of the unresolved problems faced in the construction of intelligent tutoring systems is the acquisition of background knowledge, either for the specification of the teaching strategy, or for the construction of the student model, identifying the deviations of students' behavior. In this paper, we argue that the use of sequential pattern mining and constraint relaxations can be used to automatically acquire that knowledge. We show that the methodology of constrained pattern mining used can solve this problem in a way that is difficult to achieve with other approaches.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Extracting loosely structured data records through mining strict patterns

Extracting loosely structured data records through mining st...

引用

24th IEEE international Conference on data Engineering/ 1st international workshop on Secure Semantic Web

作者： Wu, Yipu Chen, Jing Li, Qing City Univ Hong Kong Dept Comp Sci 83 Tat Chee Avenne Kowloon Hong Kong Peoples R China

ISBN: (纸本)9781424418367

Extracting loosely structured data records (DRs) has wide applications in many domains, such as forum pattern recognition, blog data analysis, and books and news review analysis. Currently existing methods work well for strongly structured DRs only. In this paper, we address the problem of extracting loosely structured DRs through mining strict patterns. In our method, we utilize both content feature and tag tree feature to recognize the loosely structured DRs, and propose a new approach to extract the DRs automatically. Through experimental study we demonstrate that this method is both effective and robust in practice.

关键词： pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Proceedings - 1st international workshop on Knowledge Discovery and data mining, WKDD

Proceedings - 1st International Workshop on Knowledge Discov...

引用

1st international workshop on Knowledge Discovery and data mining, WKDD

ISBN: (纸本)0769530907

The proceedings contain 145 papers. The topics discussed include: advancing knowledge discovery and data mining;knowledge management in the ubiquitous software development;a novel network intrusion detection system (NIDS) based on signatures search of data mining;mining high utility itemsets in large high dimensional data;effective pruning strategies for sequential pattern mining;cooperation forensic computing research;grasping related words of unknown word for automatic extension of lexical dictionary;a novel website structure optimization model for more effective web navigation;average fuzzy direction based handwritten Chinese characters recognition approach;the data mining technology based on CIMS and its application on automotive remanufacturing;an empirical study on improving the manufacturing informatization index system of China;and centrality research on the traditional Chinese medicine network.

关键词： Knowledge management

来源：评论

学校读者我要写书评

暂无评论

Proceeding of the 1st international workshop on Image mining Theory and Application, IMTA 2008 - In Conjuction with VIISIGRAPP 2008

Proceeding of the 1st International Workshop on Image Mining...

引用

1st international workshop on Image mining Theory and Applications, IMTA 2008 - In Conjunction with VIISIGRAPP 2008

ISBN: (纸本)9789898111258

The proceedings contain 14 papers. The topics discussed include: descriptive analysis of image data: basic models;media analysis and the algorithm ontology;descriptive approach to medical image analysis- substantiation and interpretation;shape modeling for the analysis of heart deformation patterns;fast multi-view evaluation of data represented by symmetric clusters;search algorithm and the distortion analysis of fine details of real images;a proposal for automatic inference of pressure ulcers grade based on wound images and patient data;an image mining medical warehouse;geo-located image categorization and location recognition;pearling: stroke segmentation with crusted pearl strings;automatic target retrieval in a video surveillance task;and learning probabilistic models for recognizing faces under pose variations.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The evaluation of wavelet and data driven feature selection for image understanding

The evaluation of wavelet and data driven feature selection ...

引用

1st international Conference on Biomedical Engineering and Informatics

作者： Liu Jinshuo Zhang Dengyi Liu Siwen Fang Ying Zhang Ming Wuhan Univ Comp Sch Hubei Peoples R China Urban Construct Sch Jilin Dept Mech Engn Jilin Peoples R China Wuhan Huanpi Kaidi Water Serv Co Ltd Wuhan Peoples R China

ISBN: (纸本)9780769531182

Image mining with pattern recognition methods has been widely used to understand the image knowledge. The general rule for the number of features is that the number can not be too much to improve the efficiency and speed of mining. This paper presents the wavelet methods for feature compression, and gives the evaluation of the wavelet methods comparing with data driven feature selection using the example of the project of image mining of pathogen yeast Cryptococcus Neoformans. The experiments show that the wavelet methods for feature compression are almost as effective as data driven feature selection to identify variance pathogen condition. The experiments are built on the training images set and evaluated using the new test set images with machine learning tool WEKA.

关键词： wavelet image mining data driven feature compression

来源：评论

学校读者我要写书评

暂无评论

pattern classification by a Gibbsian Kohonen neural network with an application to Arabic character recognition

Pattern classification by a Gibbsian Kohonen neural network ...

引用

1st international workshop on Image Processing Theory

作者： Mezghani, N. Mitiche, A. Hop Notre Dame Bon Secours Ctr Rech CHUM Lab Rech Imagerie & Orthoped LIO Montreal PQ H2L 4K8 Canada INRS EMT Institut Montreal PQ Canada

ISBN: (纸本)9781424433216

Recent studies have shown that the Gibbs density function can model complex patterns and that a constrained maximum entropy formulation affords a powerful means of estimating its parameters from pattern class data. The theory, developed in the context of learning a prior model of natural images, has been applied successfully to the synthesis of textures and shapes, and to pattern classification. The basic parameter estimation algorithm rests on gradient algorithm following the maximization under constraints of an entropy criterion. The purpose of this study is to investigate a Gibbsian Kohonen neural network, a Kohonen network which can learn these constrained maximum entropy Gibbs density parameters for pattern representation and classification. Experiments in classification of handwritten characters verify the validity and efficiency of the method.

关键词： Kohonen neural network Gibbs density parameter estimation pattern classification handwritten characters

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：