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检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3334 条记录，以下是2771-2780 订阅

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Drugs and Drug-Like Compounds: Discriminating Approved Pharmaceuticals from Screening-Library Compounds

Drugs and Drug-Like Compounds: Discriminating Approved Pharm...

引用

4th international conference pattern recognition in Bioinformatics

作者： Schierz, Amanda C. King, Ross D. Bournemouth Univ Software Syst Res Grp Poole HouseTalbot Campus Poole BH12 5BB Dorset England Aberystwyth Univ Computat Biol Res Grp Aberystwyth SY23 3DB Dyfed Wales

ISBN: (纸本)9783642040306

Compounds in drug screening-libraries should resemble pharmaceuticals. To operationally test this, we analysed the compounds in terms of known drug-like filters and developed a novel machine learning method to discriminate approved pharmaceuticals from "drug-like" compounds. this method uses both structural features and molecular properties for discrimination. the method has an estimated accuracy of 91% in discriminating between the Maybridge Hit-Finder library and approved pharmaceuticals, and 99% between the NATDiverse collection (from Analyticon Discovery) and approved pharmaceuticals. these results show that Lipinski's Rule of 5 for oral absorption is not Sufficient to describe "drug-likeness" and be the main basis of screening-library design.

关键词： Inductive Logic Programming drug-likeness machine learning Rule of 5 compound screening library

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A Large Margin Classifier with Additional Features

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6th international conference on machine learning and data mining in pattern recognition

作者： Liu, Xinwang Yin, Jianping Zhu, En Zhang, Guomin Zhan, Yubin Li, Miaomiao Natl Univ Def Technol Sch Comp Sci Changsha 410073 Hunan Peoples R China Kunming Univ Sci & Technol Coll Informat Engn & Automat Kunming 650216 Yunnan Peoples R China

ISBN: (纸本)9783642030697

We consider the problem of learning classifiers from samples which have additional features that are absent due to noise or corruption of measurement. the common approach for handling missing features in discriminative models is first to complete their unknown values, anti then a standard classification algorithm is employed over the completed data. In this paper, an algorithm which aims to maximize the margin of each sample in its own relevant subspace is proposed. We show how incomplete data can be classified directly without completing any missing features in a large-margin learning framework. Moreover, according to the theory of optimal kernel function, we proposed an optimal kernel function which is a convex composition of a set of linear kernel function to measure the similarity between additional features of each two samples. Based on the geometric interpretation of the margin, we formulate an objective function to maximize the margin of each sample in its own relevant subspace. In this formulation. we make use of the Structural parameters trained front existing features and optimize the structural parameters trained front additional features only. A two-step iterative procedure for solving, the objective function is proposed. By avoiding the pre-processing phase in which the data is completed, our algorithm Could offer considerable computational saving. We demonstrate our results on a number of standard benchmarks from UCI and the results Show that our algorithm can achieve better or comparable classification accuracy compared to the existing algorithms.

关键词： Large Margin Framework Incremental Missing Features learning Support Vector machine Kernel Method

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A study on Japanese historical character recognition using modular neural networks

A study on Japanese historical character recognition using m...

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2009 4th international conference on Innovative Computing, Information and Control, ICICIC 2009

作者： Horiuchi, Tadashi Kato, Satoru Department of Control Engieering Matsue College of Technology 14-4 Nishi-ikuma Matsue 690-8518 Japan Department of Information Engieering Matsue College of Technology 14-4 Nishi-ikuma Matsue 690-8518 Japan

ISBN: (纸本)9780769538730

It is fundamental work to translate the historical characters called "kuzushi-ji" into the contemporary characters in Japanese historical studies. In this paper, we develop the japanese historical character recognition system using the directional element features and modular neural networks. Modular neural networks consist of two kinds of classifiers: a roughclassifier to find the several candidates of categories for the input pattern, and a set of fine-classifiers that determine the category of the input pattern as the final result of character recognition. We construct the rough-classifier using the Self-Organizing Maps (SOM), which can derive the multi-templates for each category from input data. the fine-classifiers are realized using multi-layered neural networks, each of which solves the two-category classification problem. We also use the rough-classifier for the selection the training samples in the learning process of multilayered neural networks in order to reduce the learning time. through the experiments of historical character recognition for 57 character categories, we confirmed the effectiveness of our proposed method compared with the conventional research. © 2009 IEEE.

关键词： Character recognition

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pattern recognition and Image Analysis - 4th Iberian conference, IbPRIA 2009, Proceedings

Pattern Recognition and Image Analysis - 4th Iberian Confere...

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4th Iberian conference on pattern recognition and Image Analysis, IbPRIA 2009

ISBN: (纸本)3642021719

the proceedings contain 65 papers. the topics discussed include: inference and learning for active sensing, experimental design and control;large scale online learning of image similarity through ranking;inpainting ideas for image compression;smoothed disparity maps for continuous American sign language recognition;human action recognition using optical flow accumulated local histograms;trajectory modeling using mixtures of vector fields;high speed human detection using a multiresolution cascade of histograms of oriented gradients;face-to-face social activity detection using data collected with a wearable device;estimating vehicle velocity using Image profiles on rectified images;kernel based multi-object tracking using gabor functions embedded in a region covariance matrix;and autonomous configuration of parameters in robotic digital cameras.

关键词： Optical flows

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Assessing the Eligibility of Kidney Transplant Donors

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6th international conference on machine learning and data mining in pattern recognition

作者： Reinaldo, Francisco Fernandes, Carlos Rahman, Md Anishur Malucelli, Andreia Camacho, Rui Univ Porto FEUP Rua Dr Roberto FriasSn P-4200465 Porto Portugal Bairro Univ UnilesteMG Ctr Univ Leste Minas Gerais GIC BR-3571056 Coronel Fabriciano MG Brazil Pontificia Univ Catolica Parana PUCPR PostGrad Programme Hlth Technol PPGTS BR-215901 Curitiba Parana Brazil Pontificia Univ Catolica Parana PUCPR PostGrad Programme Hlth Technol PPGTS BR-80215901 Curitiba Parana Brazil Univ Porto FEUP Porto 4200465 Portugal

ISBN: (纸本)9783642030697

Organ transplantation is a highly complex decision process that requires expert, decisions. the major problem ill a transplantation procedure is the possibility of the receiver's immune system attack and destroy the transplanted tissue. It is therefore of capital importance to find a donor with the highest possible compatibility with the receiver, and thus reduce rejection. Finding a good donor is not a straightforward task because a complex network of relations exist's between the immunological and the clinical variables that, influence the receivers acceptance of the transplanted organ. Currently the process of analyzing these variables involves a careful study by the clinical transplant team. the number and complexity of the relations between variables make the manual process very slow. Ill this paper we propose and compare two machine learning algorithms that might help the transplant team ill improving and Speeding up their decisions. We achieve that objective by analyzing past real cases and constructing models as set, of rules. Such models are accurate and understandable by experts.

关键词： learning algorithms

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learning betting tips from users' bet selections

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6th international conference on machine learning and data mining in pattern recognition, MLDM 2009

作者： Štrumbelj, Erik Šikonja, Marko Robnik Kononenko, Igor Faculty of Computer and Information Science University of Ljubljana Trzaška 25 Ljubljana 1000 Slovenia

ISBN: (纸本)3642030696

In this paper we address the problem of using bet selections of a large number of mostly non-expert users to improve sports betting tips. A similarity based approach is used to describe individual users' strategies and we propose two different scoring functions to evaluate them. the information contained in users' bet selections improves on using only bookmaker odds. Even when only bookmaker odds are used, the approach gives results comparable to those of a regression-based forecasting model. © 2009 Springer Berlin Heidelberg.

关键词： data mining

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An evaluation framework for pervasive healthcare applications 4

An evaluation framework for pervasive healthcare application...

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4th international conference on Broadband Communication, Information Technology and Biomedical Applications, BROADBANDCOM 2009

作者： Kurschl, Werner Mitsch, Stefan Schönböck, Johannes Upper Austria University of Applied Sciences School of Informatics Communications and Media HagenbergA-4232 Austria

ISBN: (纸本)9788374934701

Pervasive healthcare applications help to improve elderly and needy persons habitability by assisting them in living autonomously, and letting them participate in social communities and family life. these applications are often highly complex. the data gained from many wireless sensors running on different sensor platforms is usually further processed and interpreted by machine learning and pattern recognition components. the complexity of these systems stems from different types of environmental and vital parameters, different sampling rates, heterogeneous sensor platforms, unreliable network connections, as well as different programming languages that must be tailored to the use-case and the application environment. For further development decisions and to improve existing pervasive healthcare applications is the analysis and evaluation of known approaches a well known method. In this paper we present an evaluation framework for pervasive healthcare applications, which allows us to separate different approaches and discuss important aspects.

关键词： pattern recognition

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Advances in Intelligent data Analysis VIII 2009

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丛书名： Lecture Notes in Computer Science

2009年

作者： Niall M. Adams Céline Robardet Arno Siebes Jean-Francois Boulicaut

ISBN: (数字)9783642039157

ISBN: (纸本)9783642039140

this book constitutes the refereed proceedings of the 8th international conference on Intelligent data Analysis, IDA 2009, held in Lyon, France, August 31 September 2, 2009. the 33 revised papers, 18 full oral presentations and 15 poster and short oral presentations, presented were carefully reviewed and selected from almost 80 submissions. All current aspects of this interdisciplinary field are addressed; for example interactive tools to guide and support data analysis in complex scenarios, increasing availability of automatically collected data, tools that intelligently support and assist human analysts, how to control clustering results and isotonic classification trees. In general the areas covered include statistics, machine learning, data mining, classification and pattern recognition, clustering, applications, modeling, and interactive dynamic data visualization.

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A comparative study on generalization of semantic roles in FrameNet

A comparative study on generalization of semantic roles in F...

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Joint conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th international Joint conference on Natural Language Processing of the AFNLP, ACL-IJCNLP 2009

作者： Matsubayashi, Yuichiroh Okazaki, Naoaki Tsujii, Jun'ichi Department of Computer Science University of Tokyo Japan School of Computer Science University of Manchester United Kingdom National Centre for Text Mining United Kingdom

ISBN: (纸本)9781617382581

A number of studies have presented machine-learning approaches to semantic role labeling with availability of corpora such as FrameNet and PropBank. these corpora define the semantic roles of predicates for each frame independently. thus, it is crucial for the machine-learning approach to generalize semantic roles across different frames, and to increase the size of training instances. this paper explores several criteria for generalizing semantic roles in FrameNet: role hierarchy, human-understandable descriptors of roles, semantic types of filler phrases, and mappings from FrameNet roles to thematic roles of VerbNet. We also propose feature functions that naturally combine and weight these criteria, based on the training data. the experimental result of the role classification shows 19.16% and 7.42% improvements in error reduction rate and macro-averaged F1 score, respectively. We also provide in-depth analyses of the proposed criteria. © 2009 ACL and AFNLP.

关键词： learning systems

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Class Prediction from Disparate Biological data Sources Using an Iterative Multi-Kernel Algorithm

Class Prediction from Disparate Biological Data Sources Usin...

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4th international conference pattern recognition in Bioinformatics

作者： Ying, Yiming Cambell, Colin Damoulas, theodoros Girolami, Mark Univ Bristol Dept Engn Math Bristol BS8 1TR Avon England Univ Glasgow Dept Comp Sci Glasgow G12 8QQ Lanark Scotland

ISBN: (纸本)9783642040306

For many biomedical modelling tasks a number of different types of data may influence predictions made by the model. Ail established approach to pursuing supervised learning with multiple types of data, is to encode these different types of data into separate kernels and use multiple kernel learning. In this paper we propose a, simple iterative approach to multiple kernel learning (MKL), focusing on multi-class classification. this approach uses a block L-1-regularization term leading to a jointly convex formulation. It solves a standard multi-class classification problem for a single kernel, and then updates the kernel combinatorial coefficients based oil mixed RKHS norms. As opposed to other MKL approaches, our iterative approach delivers a largely ignored message that MKL does not require sophisticated optimization methods while keeping competitive training times and accuracy across a variety of problems. We show that the proposed method outperforms state-of-the-art results oil all important protein fold prediction dataset and gives competitive performance on a protein subcellular localization task.

关键词： Multiple kernel learning multi-class bioinformatics protein fold prediction protein subcellular localization

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