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Kernels For Structured Data

Kernels for Structured Data PDF
Author: Thomas Gartner
Publisher: World Scientific
ISBN: 9812814566
Size: 76.26 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 216
View: 2001

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Kernels For Structured Data

by Thomas Gartner, Kernels For Structured Data Books available in PDF, EPUB, Mobi Format. Download Kernels For Structured Data books, This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers.




Kernel Methods For Graph Structured Data Analysis

Kernel Methods for Graph structured Data Analysis PDF
Author: Zhen Zhang (Electrical engineer)
Publisher:
ISBN:
Size: 53.27 MB
Format: PDF, ePub
Category : Electronic dissertations
Languages : en
Pages : 121
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Kernel Methods For Graph Structured Data Analysis

by Zhen Zhang (Electrical engineer), Kernel Methods For Graph Structured Data Analysis Books available in PDF, EPUB, Mobi Format. Download Kernel Methods For Graph Structured Data Analysis books, Structured data modeled as graphs arise in many application domains, such as computer vision, bioinformatics, and sociology. In this dissertation, we focus on three important topics in graph-structured data analysis: graph comparison, graph embeddings, and graph matching, for all of which we propose effective algorithms by making use of kernel functions and the corresponding reproducing kernel Hilbert spaces.For the first topic, we develop effective graph kernels, named as "RetGK," for quantitatively measuring the similarities between graphs. Graph kernels, which are positive definite functions on graphs, are powerful similarity measures, in the sense that they make various kernel-based learning algorithms, for example, clustering, classification, and regression, applicable to structured data. Our graph kernels are obtained by two-step embeddings. In the first step, we represent the graph nodes with numerical vectors in Euclidean spaces. To do this, we revisit the concept of random walks and introduce a new node structural role descriptor, the return probability feature. In the second step, we represent the whole graph with an element in reproducing kernel Hilbert spaces. After that, we can naturally obtain our graph kernels. The advantages of our proposed kernels are that they can effectively exploit various node attributes, while being scalable to large graphs. We conduct extensive graph classification experiments to evaluate our graph kernels. The experimental results show that our graph kernels significantly outperform state-of-the-art approaches in both accuracy and computational efficiency.For the second topic, we develop scalable attributed graph embeddings, named as "SAGE." Graph embeddings are Euclidean vector representations, which encode the attributed and the topological information. With graph embeddings, we can apply all the machine learning algorithms, such as neural networks, regression/classification trees, and generalized linear regression models, to graph-structured data. We also want to highlight that SAGE considers both the edge attributes and node attributes, while RetGK only considers the node attributes. "SAGE" is a extended work of "RetGK," in the sense that it is still based on the return probabilities of random walks and is derived from graph kernels. But "SAGE" uses a totally different strategy, i.e., the "distance to kernel and embeddings" algorithm, to further represent graphs. To involve the edge attributes, we introduce the adjoint graph, which can help convert edge attributes to node attributes. We conduct classification experiments on graphs with both node and edge attributes. "SAGE" achieves the better performances than all previous methods.For the third topic, we develop a new algorithm, named as "KerGM," for graph matching. Typically, graph matching problems can be formulated as two kinds of quadratic assignment problems (QAPs): Koopmans-Beckmann's QAP or Lawler's QAP. In our work, we provide a unifying view for these two problems by introducing new rules for array operations in Hilbert spaces. Consequently, Lawler's QAP can be considered as the Koopmans-Beckmann's alignment between two arrays in reproducing kernel Hilbert spaces, making it possible to efficiently solve the problem without computing a huge affinity matrix. Furthermore, we develop the entropy-regularized Frank-Wolfe algorithm for optimizing QAPs, which has the same convergence rate as the original Frank-Wolfe algorithm while dramatically reducing the computational burden for each outer iteration. Furthermore, we conduct extensive experiments to evaluate our approach, and show that our algorithm has superior performance in both matching accuracy and scalability.




Machine Learning Ecml

Machine Learning  ECML      PDF
Author:
Publisher:
ISBN:
Size: 48.70 MB
Format: PDF, ePub, Mobi
Category : Induction (Logic)
Languages : en
Pages :
View: 3755

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Machine Learning Ecml

by , Machine Learning Ecml Books available in PDF, EPUB, Mobi Format. Download Machine Learning Ecml books,




Machine Learning

Machine Learning PDF
Author: Claude Sammut
Publisher: Morgan Kaufmann
ISBN:
Size: 62.35 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 706
View: 218

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Machine Learning

by Claude Sammut, Machine Learning Books available in PDF, EPUB, Mobi Format. Download Machine Learning books, Proceedings of the annual International Conferences on Machine Learning, 1988-present. Current volume: ICML 2002: 19th International Conference on Machine Learning. Submissions are expected that describe empirical, theoretical, and cognitive-modeling research in all areas of machine learning. Submissions that present algorithms for novel learning tasks, interdisciplinary research involving machine learning, or innovative applications of machine learning techniques to challenging, real-world problems are especially encouraged.




Proceedings

Proceedings PDF
Author: American Association for Artificial Intelligence
Publisher: Amer Assn for Artificial
ISBN:
Size: 64.55 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 1993
View: 2235

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Proceedings

by American Association for Artificial Intelligence, Proceedings Books available in PDF, EPUB, Mobi Format. Download Proceedings books,




Effective Statistical Models For Syntactic And Semantic Disambiguation

Effective Statistical Models for Syntactic and Semantic Disambiguation PDF
Author: Kristina Nikolova Toutanova
Publisher:
ISBN:
Size: 45.76 MB
Format: PDF, ePub
Category :
Languages : en
Pages : 170
View: 6736

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Effective Statistical Models For Syntactic And Semantic Disambiguation

by Kristina Nikolova Toutanova, Effective Statistical Models For Syntactic And Semantic Disambiguation Books available in PDF, EPUB, Mobi Format. Download Effective Statistical Models For Syntactic And Semantic Disambiguation books,




Ijcai 05

IJCAI 05 PDF
Author: Leslie Pack Kaelbling
Publisher:
ISBN:
Size: 34.70 MB
Format: PDF, Kindle
Category : Artificial intelligence
Languages : en
Pages : 1779
View: 4579

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Ijcai 05

by Leslie Pack Kaelbling, Ijcai 05 Books available in PDF, EPUB, Mobi Format. Download Ijcai 05 books,




Deutsche Nationalbibliographie Und Bibliographie Der Im Ausland Erschienenen Deutschsprachigen Ver Ffentlichungen

Deutsche Nationalbibliographie und Bibliographie der im Ausland erschienenen deutschsprachigen Ver  ffentlichungen PDF
Author:
Publisher:
ISBN:
Size: 59.62 MB
Format: PDF, ePub, Mobi
Category : Dissertations, Academic
Languages : de
Pages :
View: 7129

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Deutsche Nationalbibliographie Und Bibliographie Der Im Ausland Erschienenen Deutschsprachigen Ver Ffentlichungen

by , Deutsche Nationalbibliographie Und Bibliographie Der Im Ausland Erschienenen Deutschsprachigen Ver Ffentlichungen Books available in PDF, EPUB, Mobi Format. Download Deutsche Nationalbibliographie Und Bibliographie Der Im Ausland Erschienenen Deutschsprachigen Ver Ffentlichungen books,




Acl 2007

ACL 2007 PDF
Author: Association for Computational Linguistics. Meeting
Publisher:
ISBN:
Size: 28.64 MB
Format: PDF, ePub
Category : Computational linguistics
Languages : en
Pages :
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Acl 2007

by Association for Computational Linguistics. Meeting, Acl 2007 Books available in PDF, EPUB, Mobi Format. Download Acl 2007 books,