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检索条件"任意字段=1st International Workshop on Machine Learning and Data Mining in Pattern Recognition"

共 580 条记录，以下是481-490 订阅

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learning to rank typed graph walks: Local and global approaches

Learning to rank typed graph walks: Local and global approac...

引用

Joint 9th WebKDD and 1st SNA-KDD workshop 2007 on Web mining and Social Network Analysis. Held in conjunction with 13th ACM SIGKDD international Conference on Knowledge Discovery and data mining, KDD 2007

作者： Minkov, Einat Cohen, William W. Language Technologies Institute Carnegie Mellon University Pittsburgh PA Machine Learning Department Carnegie Mellon University Pittsburgh PA

ISBN: (纸本)9781595938480

We consider the setting of lazy random graph walks over directed graphs, where entities are represented as nodes and typed edges represent the relations between them. This framework has been used in a variety of problems to derive an extended measure of entity similarity. In this paper we contrast two different approaches for applying supervised learning in this framework to improve graph walk performance: a gradient descent algorithm that tunes the transition probabilities of the graph, and a reranking approach that uses features describing global properties of the traversed paths. An empirical evaluation on a set of tasks from the domain of personal information management and multiple corpora show that reranking performance is usually superior to the local gradient descent algorithm, and that the methods often yield best results when combined. Copyright 2007 ACM.

关键词： Graph theory

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Acoustic features extraction for emotion recognition

Acoustic features extraction for emotion recognition

引用

6th IEEE/ACIS international Conference on Computer and Information Science in conjunction with 1st IEEE/ACIS international workshop on e-Activity

作者： Rong, Jia Chen, Yi-Ping Phoebe Chowdhury, Morshed Li, Gang Deakin Univ Sch Informat Technol & Engn Melbourne Vic Australia

ISBN: (纸本)9780769528410

In the last decade, the efforts of spoken language processing have achieved significant advances, however, the work with emotional recognition has not progressed so far,, and can only achieve 50% to 60% in accuracy [1]. This is because a majority of researchers in this field have focused on the synthesis of emotional speech rather than focusing on automating human emotion recognition. Many research groups have focused on how to improve the performance of the classifier they used for emotion recognition, and few work has been done on data pre-processing, such as the extraction and selection of a set of specifying acoustic features instead of using all the possible ones they had in hand. To work with well-selected acoustic features does not mean to delay the whole job, but this will save much time and resources by removing the irrelative information and reducing the high-dimension data calculation. In this paper, we developed an automatic feature selector based on a RF2TREE algorithm and the traditional C4.5 algorithm. RF2TREE applied here helped us to solve the problems that did not have enough data examples. The ensemble learning technique was applied to enlarge the original data set by building a bagged random forest to generate many virtual examples, and then the new data set was used to train a single decision tree, which selects the most efficient features to represent the speech signals for the emotion recognition. Finally, the output of the selector was a set of specifying acoustic features, produced by RF2TREE and a single decision tree.

关键词： feature extraction machine learning ensemble learning twice learning random forest decision tree

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A comparison study on algorithms of detecting long forms for short forms in biomedical text

A comparison study on algorithms of detecting long forms for...

引用

1st international workshop on Text mining in Bioinformatics

作者： Torii, Manabu Hu, Zhang-zhi Song, Min Wu, Cathy H. Liu, Hongfang Georgetown Univ Med Ctr Dept Biostat Bioinformat & Biomath Washington DC 20057 USA Georgetown Univ Med Ctr Dept Biochem & Mol & Cell Biol Washington DC 20007 USA Univ Hts New Jersey Inst Technol Dept Informat Syst Newark NJ 07102 USA

Motivation: With more and more research dedicated to literature mining in the biomedical domain, more and more systems are available for people to choose from when building literature mining applications. In this study, we focus on one specific kind of literature mining task, i.e., detecting definitions of acronyms, abbreviations, and symbols in biomedical text. We denote acronyms, abbreviations, and symbols as short forms (SFs) and their corresponding definitions as long forms (LFs). The study was designed to answer the following questions;i) how well a system performs in detecting LFs from novel text, ii) what the coverage is for various terminological knowledge bases in including SFs as synonyms of their LFs, and iii) how to combine results from various SF knowledge bases. Method: We evaluated the following three publicly available detection systems in detecting LFs for SFs: i) a handcrafted pattern/rule based system by Ao and Takagi, ALICE, ii) a machine learning system by Chang et al., and iii) a simple alignment-based program by Schwartz and Hearst. In addition, we investigated the conceptual coverage of two terminological knowledge bases: i) the UMLS (the Unified Medical Language System), and ii) the BioThesaurus (a thesaurus of names for all UniProt protein records). We also implemented a web interface that provides a virtual integration of various SF knowledge bases. Results: We found that detection systems agree with each other on most cases, and the existing terminological knowledge bases have a good coverage of synonymous relationship for frequently defined LFs. The web interface allows people to detect SF definitions from text and to search several SF knowledge bases.

关键词： learning systems

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Biological Lateral Inhibition and Digital Cellular Neural Network Applied on Associational Memory

Biological Lateral Inhibition and Digital Cellular Neural Ne...

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international Conference on Bioinformatics and Biomedical Engineering (ICBBE)

作者： Fang Zi Ke Zhang Yanjun Li Dawei Zhao College of AstronauticsNorthwestern Polytechnical UniversityXi’an P.R.China Laboratory of Image ProcessingTianjin Computing Technology InstituteTianjin P.R.China

The lateral inhibition mechanism of organisms and digital cellular neural network (DCNN) are introduced. Then the integration of them is studied. Referenced some beneficial conclusions of DCNN, the model of digital acyclic lateral inhibition network (DALIN) is proposed in the paper. Until now most of existing associational memory algorithm only can operate on the two-value state {-1, +1}. Enlightened by these memory algorithms, especially by the DCNN, a new associational memory algorithm is proposed based on the DALIN. The new algorithm can operate on gray image, which includes 256 states, namely from 0 to 255. The implementation condition of the new algorithm is proposed and proved. Then it's applied on a cell's gray image with some noise. The results show that noises in images can be effectively filtered with the new algorithm. The learning effect on input samples is perfect. The calculation quantity of weight values is decreased and the learning time is shortened. Images processed by lateral inhibition networks in the domain of space are accorded with the requirement of human beings' vision. The new algorithm is significative for the learning and pattern recognition of images, such as cell recognition and X- ray diagnosis. The DALIN also can be applied in other domain of image process, such as edge extraction and scene matching.

关键词： Cellular neural networks pattern recognition Organisms Artificial neural networks Humans Image processing Nervous system Information processing Artificial intelligence data mining

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Finding the Right Features for Instrument Classification of Classical Music 06

<bold>Finding the Right Features for Instrument Classificati...

引用

1st international workshop international AI and data mining

作者： Deng, Da Simmermacher, Christian Cranefield, stephen Univ Otago Dept Informat Sci POB 56 Dunedin New Zealand

ISBN: (纸本)9780769527307

In tackling data mining and pattern recognition tasks, finding a compact but effective set of features is often a crucial step in the whole problem solving process. In this paper we present an empirical study on feature selection for classical instrument recognition, using machine learning techniques to select and evaluate features extracted from a number of different feature schemes in terms of their classification performance. It is revealed that there is significant redundancy in existing feature schemes commonly used in practice. Our results suggest that further feature analysis research is necessary for optimising feature selection for the instrument recognition problem.

关键词： data mining

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Integrating Al and data mining - 1st international workshop Proceedings, AIDM 2006

Integrating Al and Data Mining - 1st International Workshop ...

引用

1st international workshop on Integrating AI and data mining, AIDM 2006

ISBN: (纸本)0769527302

The proceedings contain 10 papers. The topics discussed include: pattern of behavior mediated by cognitive scripts and emotional attitudes - context aware engineering of data mining systems;visualization of non-vectorial data using twin kernel embedding;dynamic algorithm selection using reinforcement learning;mining better technical trading strategies with genetic algorithms;finding the right features for instrument classification of classical music;quantification of intermarker influence based on global optimization and its application for stock market prediction;efficient fuzzy rules for classification;using coupled hidden Markov models for model suspect interactions in digital forensic analysis;using ontology to map categories in blog;and computational quantification of trust updates.

关键词： data mining

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PCA-based data mining probabilistic and fuzzy approaches with applications in pattern recognition

PCA-based data mining probabilistic and fuzzy approaches wit...

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1st international Conference on Software and data Technologies

作者： state, Luminita Cocianu, Catalina Vlamos, Panayiotis stefanescu, Viorica Univ Pitesti Dept Comp Sci Caderea Bastliei 45 Bucharest 1 Romania Acad Econ Studies Dept Comp Sci Bucharest 1 Romania Ionian Univ Corfu Greece Acad Econ Studies Dept Math Bucharest 1 Romania

ISBN: (纸本)9728865694

The aim of the paper is to develop a new learning by examples PCA-based algorithm for extracting skeleton information from data to assure both good recognition performances, and generalization capabilities. Here the generalization capabilities are viewed twofold, on one hand to identify the right class for new samples coming from one of the classes taken into account and, on the other hand, to identify the samples coming from a new class. The classes are represented in the measurement/feature space by continuous repartitions, that is the model is given by the family of density functions( f(h))(h is an element of H) where H stands for the finite set of hypothesis (classes). The basis of the learning process is represented by samples of possible different sizes coming from the considered classes. The skeleton of each class is given by the principal components obtained for the corresponding sample.

关键词： principal component analysis fuzzy clustering supervised learning cluster analysis pattern recognition data mining

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Automatic annotation of protein functional class from sparse and imbalanced data sets

引用

1st international workshop on data mining and Bioinformatics

作者： Jung, Jaehee Thon, Michael R. Texas A&M Univ Dept Comp Sci College Stn TX 77843 USA Texas A&M Univ Dept Plant Pathol & Microbiol College Stn TX 77843 USA

ISBN: (纸本)9783540689706

In recent years, high-throughput genome sequencing and sequence analysis technologies have created the need for automated annotation and analysis of large sets of genes. The Gene Ontology (GO) provides a common controlled vocabulary for describing gene function however the process for annotating proteins with GO terms is usually through a tedious manual curation process by trained professional annotators. With the wealth of genomic data that are now available, there is a need for accurate automated annotation methods. In this paper, we propose a method for automatically predicting GO terms for proteins by applying statistical pattern recognition techniques. We employ protein functional domains as features and learn independent Support Vector machine classifiers for each GO term. This approach creates sparse data sets with highly imbalanced class distribution. We show that these problems can be overcome with standard feature and instance selection methods. We also present a meta-learning scheme that utilizes multiple SVMs trained for each GO term, resulting in improved overall performance than either SVM can achieve alone. The implementation of the tool is available at http://***/AAPFC.

关键词： gene annotation feature selection gene ontology InterPro imbalanced data

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Discovering consensus patterns in biological databases

引用

1st international workshop on data mining and Bioinformatics

作者： ElTabakh, Mohamed Y. Aref, Walid G. Ouzzani, Mourad Ali, Mohamed H. Purdue Univ Dept Comp Sci W Lafayette IN 47906 USA Purdue Univ Cyber Ctr W Lafayette IN 47906 USA

ISBN: (纸本)9783540689706

Consensus patterns, like motifs and tandem repeats, are highly conserved patterns with very few substitutions where no gaps are allowed. In this paper, we present a progressive hierarchical clustering technique for discovering consensus patterns in biological databases over a certain length range. This technique can discover consensus patterns with various requirements by applying a post-processing phase. The progressive nature of the hierarchical clustering algorithm makes it scalable and efficient. Experiments to discover motifs and tandem repeats on real biological databases show significant performance gain over non-progressive clustering techniques.

关键词： pattern recognition

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Automatic chord recognition from audio using a supervised HMM trained with audio-from-symbolic data

Automatic chord recognition from audio using a supervised HM...

引用

1st ACM workshop on Audio and Music Computing for Multimedia, AMCMM'06, co-located with the 2006 ACM international Multimedia Conference

作者： Lee, Kyogu Slaney, Malcolm Center for Computer Research in Music and Acoustics Music Department Stanford University Yahoo Research Sunnyvale CA 94089 United States

ISBN: (纸本)1595935010

A novel approach for obtaining labeled training data is presented to directly estimate the model parameters in a supervised learning algorithm for automatic chord recognition from the raw audio. To this end, harmonic analysis is first performed on symbolic data to generate label files. In paral-lel, we synthesize audio data from the same symbolic data, which are then provided to a machine learning algorithm along with label files to estimate model parameters. Experimental results show higher performance in frame-level chord recognition than the previous approaches. © 2006 ACM.

关键词： Speech recognition

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