Items related to Non-Standard Parameter Adaptation for Exploratory Data...
Non-Standard Parameter Adaptation for Exploratory Data Analysis: 249 (Studies in Computational Intelligence, 249) - Hardcover
Synopsis
Exploratory data analysis, also known as data mining or knowledge discovery from databases, is typically based on the optimisation of a specific function of a dataset. Such optimisation is often performed with gradient descent or variations thereof. In this book, we first lay the groundwork by reviewing some standard clustering algorithms and projection algorithms before presenting various non-standard criteria for clustering. The family of algorithms developed are shown to perform better than the standard clustering algorithms on a variety of datasets.
We then consider extensions of the basic mappings which maintain some topology of the original data space. Finally we show how reinforcement learning can be used as a clustering mechanism before turning to projection methods.
We show that several varieties of reinforcement learning may also be used to define optimal projections for example for principal component analysis, exploratory projection pursuit and canonical correlation analysis. The new method of cross entropy adaptation is then introduced and used as a means of optimising projections. Finally an artificial immune system is used to create optimal projections and combinations of these three methods are shown to outperform the individual methods of optimisation.
"synopsis" may belong to another edition of this title.
From the Back Cover
Exploratory data analysis, also known as data mining or knowledge discovery from databases, is typically based on the optimisation of a specific function of a dataset. Such optimisation is often performed with gradient descent or variations thereof. In this book, we first lay the groundwork by reviewing some standard clustering algorithms and projection algorithms before presenting various non-standard criteria for clustering. The family of algorithms developed are shown to perform better than the standard clustering algorithms on a variety of datasets.
We then consider extensions of the basic mappings which maintain some topology of the original data space. Finally we show how reinforcement learning can be used as a clustering mechanism before turning to projection methods.
We show that several varieties of reinforcement learning may also be used to define optimal projections for example for principal component analysis, exploratory projection pursuit and canonical correlation analysis. The new method of cross entropy adaptation is then introduced and used as a means of optimising projections. Finally an artificial immune system is used to create optimal projections and combinations of these three methods are shown to outperform the individual methods of optimisation.
"About this title" may belong to another edition of this title.
- PublisherSpringer
- Publication date2009
- ISBN 10 3642040047
- ISBN 13 9783642040047
- BindingHardcover
- LanguageEnglish
- Number of pages234
Other Popular Editions of the Same Title
Search results for Non-Standard Parameter Adaptation for Exploratory Data...
Stock Image
Non-Standard Parameter Adaptation for Exploratory Data Analysis (Studies in Computational Intelligence, 249)
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # ABLIING23Mar3113020214502
Quantity: Over 20 available
Stock Image
Non-Standard Parameter Adaptation for Exploratory Data Analysis (Studies in Computational Intelligence, 249)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In. Seller Inventory # ria9783642040047_new
Quantity: Over 20 available
Stock Image
Non-Standard Parameter Adaptation for Exploratory Data Analysis
Seller: Buchpark, Trebbin, Germany
Seller rating 5 out of 5 stars
Condition: Sehr gut. Zustand: Sehr gut - Neubindung, Buchschnitt leicht verkürzt, Auflage 2009 | Seiten: 228 | Sprache: Englisch | Produktart: Bücher. Seller Inventory # 5684292/12
Quantity: 1 available
Seller Image
Non-Standard Parameter Adaptation for Exploratory Data Analysis
Published by
Berlin Springer Berlin Heidelberg Springer Sep 2009, 2009
ISBN 10: 3642040047
ISBN 13: 9783642040047
New
Hardcover
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Exploratory data analysis, also known as data mining or knowledge discovery from databases, is typically based on the optimisation of a specific function of a dataset. Such optimisation is often performed with gradient descent or variations thereof. In this book, we first lay the groundwork by reviewing some standard clustering algorithms and projection algorithms before presenting various non-standard criteria for clustering. The family of algorithms developed are shown to perform better than the standard clustering algorithms on a variety of datasets.We then consider extensions of the basic mappings which maintain some topology of the original data space. Finally we show how reinforcement learning can be used as a clustering mechanism before turning to projection methods. We show that several varieties of reinforcement learning may also be used to define optimal projections for example for principal component analysis, exploratory projection pursuit and canonical correlation analysis. The new method of cross entropy adaptation is then introduced and used as a means of optimising projections. Finally an artificial immune system is used to create optimal projections and combinations of these three methods are shown to outperform the individual methods of optimisation. 223 pp. Englisch. Seller Inventory # 9783642040047
Quantity: 2 available
Seller Image
Non-Standard Parameter Adaptation for Exploratory Data Analysis
Published by
Springer Berlin Heidelberg, 2009
ISBN 10: 3642040047
ISBN 13: 9783642040047
New
Hardcover
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents novel methods of parameter adaptation in machine learningValuable contribution to create a true artificial intelligence Recent research in Reinforcement learning, cross entropy and artificial immune systems for explor. Seller Inventory # 5044176
Quantity: Over 20 available
Seller Image
Non-Standard Parameter Adaptation for Exploratory Data Analysis
Published by
Springer, Berlin, Springer Berlin Heidelberg, Springer, 2009
ISBN 10: 3642040047
ISBN 13: 9783642040047
New
Hardcover
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Exploratory data analysis, also known as data mining or knowledge discovery from databases, is typically based on the optimisation of a specific function of a dataset. Such optimisation is often performed with gradient descent or variations thereof. In this book, we first lay the groundwork by reviewing some standard clustering algorithms and projection algorithms before presenting various non-standard criteria for clustering. The family of algorithms developed are shown to perform better than the standard clustering algorithms on a variety of datasets.We then consider extensions of the basic mappings which maintain some topology of the original data space. Finally we show how reinforcement learning can be used as a clustering mechanism before turning to projection methods. We show that several varieties of reinforcement learning may also be used to define optimal projections for example for principal component analysis, exploratory projection pursuit and canonical correlation analysis. The new method of cross entropy adaptation is then introduced and used as a means of optimising projections. Finally an artificial immune system is used to create optimal projections and combinations of these three methods are shown to outperform the individual methods of optimisation. Seller Inventory # 9783642040047
Quantity: 2 available