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

共 1778 条记录，以下是1541-1550 订阅

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Analysis of binary feature mapping rules for promoter recognition in imbalanced DNA sequence datasets using Support Vector machine

Analysis of binary feature mapping rules for promoter recogn...

引用

international IEEE conference on Intelligent Systems, IS

作者： Robertas Damasevicius Software Engineering Department Kaunas University of Technology Kaunas Lithuania

recognition of specific functionally-important DNA sequence fragments is considered one of the most important problems in bioinformatics. One type of such fragments are promoters, i.e., short regulatory DNA sequences located upstream of a gene. Detection of promoters in DNA sequences is important for successful gene prediction. In this paper, a machine learning method, called support vector machine (SVM), is used for classification of DNA sequences and promoter recognition. For optimal classification, 11 rules for mapping of DNA sequences into binary SVM feature space are analyzed. Classification is performed using a power series kernel function. Kernel parameters are optimized using a modification of the Nelder-Mead (downhill simplex) optimization method. the results of classification for drosophila and human sequence datasets are presented.

关键词： DNA Sequences Support vector machines Support vector machine classification Bioinformatics learning systems Encoding Biological cells Kernel Training data

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Pre-processing aspects for complexity reduction of the QSAR problem

Pre-processing aspects for complexity reduction of the QSAR ...

引用

international IEEE conference on Intelligent Systems, IS

作者： L. Dumitriu C. Segal M-V. Craciun A. Cocu Compute Science Department Dunarea de Jos University of Galati Galati Romania the Computer Science Department Galati Romania

Predictive Toxicology (PT) is one of the newest targets of the Knowledge Discovery in databases (KDD) domain. Its goal is to describe the relationships between the chemical structure of chemical compounds and biological and toxicological processes. In real PT problems there is a very important topic to be considered: the huge number of the chemical descriptors. Irrelevant, redundant, noisy and unreliable data have a negative impact, therefore one of the main goals in KDD is to detect these undesirable proprieties and to eliminate or correct them. this assumes data cleaning, noise reduction and feature selection because the performance of the applied machine learning algorithms is strongly related with the quality of the data used. In this paper, we present some of the issues that can be taken into account for preparing data before the actual knowledge discovery is performed.

关键词： Chemical compounds data mining Toxicology pattern recognition Principal component analysis databases Noise reduction Neural networks Statistical analysis pattern analysis

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learning in networks: Complex-valued neurons, pruning, and rule extraction

Learning in networks: Complex-valued neurons, pruning, and r...

引用

international IEEE conference on Intelligent Systems, IS

作者： Jacek M. Zurada Igor Aizenberg Maciej A. Mazurowski Department of Electrical and Computer Engineering University of Louisville Louisville KY USA A and M University Texarkana Texarkana USA

this paper focuses on neural networks with complex-valued (CV) neurons as well as on selected aspects of neural networks learning, pruning and rule extraction. CV neurons can be used as versatile substitutes in real-valued perceptron networks. learning of CV layers is discussed in context of traditional multilayer feedforward architecture. Such learning is derivative-free and it usually requires networks of reduced size. Selected examples and applications of CV-networks in bioinformatics and pattern recognition are discussed. the paper also covers specialized learning techniques for logic rule extraction. Such techniques include learning with pruning, and can be used in expert systems, and other applications that rely on models developed to fit measured data.

关键词： Neurons Multi-layer neural network Artificial neural networks Neural networks Biological system modeling USA Councils Logic data mining Mathematical model Nonlinear filters

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Self learning and its Implications for machine Understanding

Self Learning and its Implications for Machine Understanding

引用

international conference on Information and Automation for Sustainability (ICIAfS)

作者： Wikum Dinalankara Daswin De Silva Damminda Alahakoon Cognitive and Connectionist Systems Laboratory Monash University VIC Australia

Contemporary machine intelligence is far from realizing prominent hallmarks of human understanding and consciousness. the primary shortcoming of current methods can be attributed to the difficulty or implausibility of foreseeing and pre-programming each and every piece of information or knowledge. Emergent intelligence methods based on principles of self learning and self organization have been successful in infusing traits of understanding in machines. this understanding is in contrast to the constrained intelligence permeated on machines by classical approaches of intelligence following supervised knowledge acquisition mechanisms. the primary objective of this paper is to review current work in emergent intelligence methods and discuss means of orchestrating these in to a practical model that resembles the process of human understanding. the paper delineates intricacies of self-learning in humans from both biological and psychological perspectives. Following a discussion of several artificial models of the human mind that have been researched and documented at the conceptual level, we propose a comparatively pragmatic approach based on a novel unsupervised learning algorithm, the GSOM algorithm. this algorithm has been successfully applied to many real world knowledge acquisition and pattern discovery problems. the paper concludes with a further discussion of research developments in emergent systems, which we perceive to be the stepping stones in the search for true machine understanding.

关键词： machine learning Humans learning systems Supervised learning Artificial intelligence Unsupervised learning Image recognition Knowledge acquisition Biological system modeling Cognition

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Advanced Intelligent Computing theories and Applications. With Aspects of Artificial Intelligence 2008

引用

丛书名： Lecture Notes in Computer Science

2008年

作者： De-Shuang Huang Donald C. Wunsch Daniel S. Levine Kang-Hyun Jo

ISBN: (数字)9783540859840

ISBN: (纸本)9783540859833

this book - in conjunction with the two volumes CCIS 0015 and LNCS 5226 - constitutes the refereed proceedings of the 4th international conference on Intelligent Computing, ICIC 2008, held in Shanghai, China, in September 2008. the 136 revised full papers were carefully reviewed and selected from 2336 submissions. the papers address all current issues in the field of intelligent computing technology, including neural networks, evolutionary computing and genetic algorithms, fuzzy systems and soft computing, particle swarm optimization and niche technology, supervised and semi-supervised learning, unsupervised and reinforcement learning, fine feature extraction methods, combinatorial and numerical optimization, neural computing and optimization, case based reasoning and autonomy-oriented computing, as well as artificial life and artificial immune systems. the volume is rounded off by three special sessions on computational analysis and data mining in biological systems, on data mining and fusion in bioinformatics, and on intelligent pattern recognition and understanding in image processing.

关键词： Computational intelligence Congresses. Computational intelligence Industrial applications Congresses. Computational biology Congresses. Bioinformatics Congresses. Cognitive neuroscience Congresses.

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Mining Protein Primary Structure data Using Committee machines Approach to Predict Protein Contact Map

Mining Protein Primary Structure Data Using Committee Machin...

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international conference on Emerging Technologies, ICET

作者： Narjes K. Habibi Kaveh Mahdaviani Mohammad H. Saraee Advanced Database Systems Isfahan University of Technology Isfahan Mathematics House Isfahan Iran

Committee machines approach has shown to be useful in different applications. Protein primary structure data contain valuable information to extract. In this paper we mine these data and predict protein contact map based on committee machines. Contact map is the simplified, two dimensional representation of protein spatial structure. Contact map prediction is of great interest due to its application in fold recognition and predicting protein tertiary structure. the results show that the performance of the committee is considerably better than a single model.

关键词： Protein engineering machine learning data mining Contacts Bioinformatics Genetic mutations Protein sequence IEEE members database systems Laboratories

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4. Semantic indexing and retrieval of video

4. Semantic indexing and retrieval of video

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international conference on pattern recognition

作者： Marcel Worring Cees Snoek University of Amsterdam

the semantic gap between the low level information that can be derived from the visual data and the conceptual view the user has of the same data is a major bottleneck in video retrieval systems. It has dictated that solutions to image and video indexing could only be applied in narrow domains using specific concept detectors, e.g., ¿sunset¿ or ¿face¿. this leads to lexica of at most 10-20 concepts. the use of multimodal indexing, advances in machine learning, and the availability of some large, annotated information sources, e.g., the TRECVID benchmark, has paved the way to increase lexicon size by orders of magnitude (now 100 concepts, in a few years 1,000). this brings it within reach of research in ontology engineering, i.e. creating and maintaining large, typically 10,000+ structured sets of shared concepts. When this goal is reached we could search for videos in our home collection or on the web based on their semantic content, we could develop semantic video editing tools, or develop tools that monitor various video sources and trigger alerts based on semantic events. this tutorial lays the foundation for these exciting new horizons. It will cover basis video analysis techniques and explain the different methods for video indexing. From there it will explore how users can be given interactive access to the data. For both indexing and interactive access TRECVID evaluations will be considered.

关键词： Indexing Detectors Video sharing Information retrieval Face detection machine learning Ontologies Maintenance engineering Monitoring Large-scale systems

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Alternative robust local embedding

Alternative robust local embedding

引用

5th international conference on Wavelet Analysis and pattern recognition

作者： Xue, Hui Chen, Song-Can Nanjing Univ Aeronaut & Astronaut Comp Sci & Engn Coll Nanjing 210016 Peoples R China

ISBN: (纸本)9781424410651

Dimensionality reduction is a significant problem in pattern recognition and thus arouses broad interest in the machine learning community. Different from the traditional linear dimensionality reduction methods, recently some nonlinear Methods have been proposed in virtue of manifold learning these methods can efficiently discover the low-dimensional nonlinear manifold in a high-dimensional data space and further preserve the manifold structure of the data points in the low-dimensional embedding space. Despite their attractive properties, these nonlinear methods are sensitive to the outliers in the data sets. Moreover, the exisiting locally nonlinear dimensionality reduction methods generally neglect the globally structural information. 117 this paper, we address these problems in the context of Locally Linear Embedding (LLE). through capturing the local and global geometry information simultaneously, we propose an alternative approach to make the local embedding relatively more robust, called as Alternative Robust Local Embedding (ARLE). It can not only suppress an unfavorable influence of the oulliers on the embedding process automatically, but also outperform LLE in 2-D data visualization due to the introduction of the global geometry. Experimental results and comparisons on both synthetic and real data sets show the effectiveness of ARLE.

关键词： robust local embedding manifold learning locally linear embedding globally geometric information

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Affine feature extraction: A generalization of the Fukunaga-Koontz transformation 1

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5th international conference on machine learning and data Mining in pattern recognition

作者： Cao, Wenbo Haralick, Robert CUNY Grad Ctr Pattern Recognit Lab Dept Comp Sci 365 5th Ave New York NY 10016 USA

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

Dimension reduction methods are often applied in machine learning and data mining problems. Linear subspace methods are the commonly used ones, such as principal component analysis (PCA), Fisher's linear discriminant analysis (FDA), et al. In this paper, we describe a novel feature extraction method for binary classification problems. Instead of finding linear subspaces, our method finds lower-dimensional affine subspaces for data observations. Our method can be understood as a generalization of the Fukunaga-Koontz Transformation. We show that the proposed method has a closed-form solution and thus can be solved very efficiently. Also we investigate the information-theoretical properties of the new method and study the relationship of our method with other methods. the experimental results show that our method, as PCA and FDA, can be used as another preliminary data-exploring tool to help solve machine learning and data mining problems.

关键词： Feature extraction

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On concentration of discrete distributions with applications to supervised learning of classifiers 1

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5th international conference on machine learning and data Mining in pattern recognition

作者： Ekdahl, Magnus Koski, Timo Linkoping Univ Dept Math SE-58183 Linkoping Sweden

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

Computational procedures using independence assumptions in various forms are popular in machine learning, although checks on empirical data have given inconclusive results about their impact. Some theoretical understanding of when they work is available, but a definite answer seems to be lacking. this paper derives distributions that maximizes the statewise difference to the respective product of marginals. these distributions are, in a sense the worst distribution for predicting an outcome of the data generating mechanism by independence. We also restrict the scope of new theoretical results by showing explicitly that, depending on context, independent ('Naive') classifiers can be as bad as tossing coins. Regardless of this, independence may beat the generating model in learning supervised classification and we explicitly provide one such scenario.

关键词： Distributed computer systems

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