Diane J. Cook – författare & böcker

Mining Graph Data

E-bok

PDF, Engelska, 2006

2 061 kr

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This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have minimal background in analyzing graph data, with this book you’ll be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets.

There is a misprint with the link to the accompanying Web page for this book. For those readers who would like to experiment with the techniques found in this book or test their own ideas on graph data, the Web page for the book should be http://www.eecs.wsu.edu/MGD.

Mining Graph Data

AvDiane J. Cook,Lawrence B. Holder

Inbunden, Engelska, 2006

1 818 kr

Skickas inom 5-8 vardagar

This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have minimal background in analyzing graph data, with this book you’ll be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets. There is a misprint with the link to the accompanying Web page for this book. For those readers who would like to experiment with the techniques found in this book or test their own ideas on graph data, the Web page for the book should be http://www.eecs.wsu.edu/MGD.

Activity Learning

Discovering, Recognizing, and Predicting Human Behavior from Sensor Data

AvDiane J. Cook,Narayanan C. Krishnan

Inbunden, Engelska, 2015

1 426 kr

Skickas inom 5-8 vardagar

Defines the notion of an activity model learned from sensor data and presents key algorithms that form the core of the fieldActivity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides an in-depth look at computational approaches to activity learning from sensor data. Each chapter is constructed to provide practical, step-by-step information on how to analyze and process sensor data. The book discusses techniques for activity learning that include the following: Discovering activity patterns that emerge from behavior-based sensor dataRecognizing occurrences of predefined or discovered activities in real timePredicting the occurrences of activitiesThe techniques covered can be applied to numerous fields, including security, telecommunications, healthcare, smart grids, and home automation. An online companion site enables readers to experiment with the techniques described in the book, and to adapt or enhance the techniques for their own use.With an emphasis on computational approaches, Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data provides graduate students and researchers with an algorithmic perspective to activity learning.

Activity Learning

Discovering, Recognizing, and Predicting Human Behavior from Sensor Data

AvNarayanan C. Krishnan,Diane J. Cook

E-bok

PDF, Engelska, 2015

1 570 kr

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Defines the notion of an activity model learned from sensor data and presents key algorithms that form the core of the field

Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides an in-depth look at computational approaches to activity learning from sensor data. Each chapter is constructed to provide practical, step-by-step information on how to analyze and process sensor data. The book discusses techniques for activity learning that include the following:

Discovering activity patterns that emerge from behavior-based sensor data Recognizing occurrences of predefined or discovered activities in real time Predicting the occurrences of activities

The techniques covered can be applied to numerous fields, including security, telecommunications, healthcare, smart grids, and home automation. An online companion site enables readers to experiment with the techniques described in the book, and to adapt or enhance the techniques for their own use.With an emphasis on computational approaches, Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data provides graduate students and researchers with an algorithmic perspective to activity learning.

Activity Learning

Discovering, Recognizing, and Predicting Human Behavior from Sensor Data

AvNarayanan C. Krishnan,Diane J. Cook

E-bok

Engelska, 2015

1 540 kr

Läs direkt efter köp

Defines the notion of an activity model learned from sensor data and presents key algorithms that form the core of the field

Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides an in-depth look at computational approaches to activity learning from sensor data. Each chapter is constructed to provide practical, step-by-step information on how to analyze and process sensor data. The book discusses techniques for activity learning that include the following:

Discovering activity patterns that emerge from behavior-based sensor data Recognizing occurrences of predefined or discovered activities in real time Predicting the occurrences of activities

The techniques covered can be applied to numerous fields, including security, telecommunications, healthcare, smart grids, and home automation. An online companion site enables readers to experiment with the techniques described in the book, and to adapt or enhance the techniques for their own use.With an emphasis on computational approaches, Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data provides graduate students and researchers with an algorithmic perspective to activity learning.

Advanced Methods for Knowledge Discovery from Complex Data

AvDiane J. Cook,Lawrence B. Holderm. fl.

E-bok

PDF, Engelska, 2006

2 036 kr

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The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the followingchapters.

Advanced Methods for Knowledge Discovery from Complex Data

AvUjjwal Maulik,Lawrence B. Holderm. fl.

Häftad, Engelska, 2010

1 681 kr

Skickas inom 10-15 vardagar

The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the followingchapters.

Advanced Methods for Knowledge Discovery from Complex Data

AvUjjwal Maulik,Lawrence B. Holderm. fl.

Inbunden, Engelska, 2005

1 681 kr

Skickas inom 10-15 vardagar

The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the followingchapters.

Del 9277 - Lecture Notes in Computer Science

Human Behavior Understanding

6th International Workshop, HBU 2015, Osaka, Japan, September 8, 2015, Proceedings

AvAlbert Ali Salah,Ben J.A. Krösem. fl.

Häftad, Engelska, 2015

454 kr

Skickas inom 10-15 vardagar

This book constitutes the proceedings of the 6th International Workshop on Human Behavior Understanding, HBU 2015, held in Osaka, Japan, in September 2015.The 11 full papers were carefully reviewed and selected from 15 initial submissions. They are organized in topical sections named: interaction with elderly, learning behavior patterns, and mobile solutions.

Human Behavior Understanding

6th International Workshop, HBU 2015, Osaka, Japan, September 8, 2015, Proceedings

AvDiane J. Cook,Ben J.A. Krosem. fl.

E-bok

PDF, Engelska, 2015

551 kr

Läs direkt efter köp

This book constitutes the proceedings of the 6th International Workshop on Human Behavior Understanding, HBU 2015, held in Osaka, Japan, in September 2015.

The 11 full papers were carefully reviewed and selected from 15 initial submissions. They are organized in topical sections named: interaction with elderly, learning behavior patterns, and mobile solutions.

Del 8 - Atlantis Ambient and Pervasive Intelligence

Human Aspects in Ambient Intelligence

Contemporary Challenges and Solutions

AvTibor Bosse,Diane J. Cookm. fl.

Inbunden, Engelska, 2013

565 kr

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This book presents recent developments is the field of human aspects in Ambient Intelligence. This field, and the associated workshop series, addresses multidisciplinary aspects of AmI with human-directed disciplines such as psychology, social science, neuroscience and biomedical sciences. The aim of the workshop series is to get researchers together from these human-directed disciplines or working on cross connections of AmI with these disciplines. The focus is on the use of knowledge from these disciplines in AmI applications, in order to support humans in their daily living in medical, psychological and social respects. The book plays important role to get modellers in the psychological, neurological, social or biomedical disciplines interested in AmI as a high-potential application area for their models. From the other side, the book may make researchers in Computer Science and Artificial and Ambient Intelligence more aware of the possibilities to incorporate more substantial knowledge from the psychological, neurological, social and biomedical disciplines in AmI architectures and applications.

Human Aspects in Ambient Intelligence

Contemporary Challenges and Solutions

AvFariba Sadri,Mark Neerincxm. fl.

E-bok

PDF, Engelska, 2013

712 kr

Läs direkt efter köp

This book presents recent developments is the field of human aspects in Ambient Intelligence. This field, and the associated workshop series, addresses multidisciplinary aspects of AmI with human-directed disciplines such as psychology, social science, neuroscience and biomedical sciences. The aim of the workshop series is to get researchers together from these human-directed disciplines or working on cross connections of AmI with these disciplines. The focus is on the use of knowledge from these disciplines in AmI applications, in order to support humans in their daily living in medical, psychological and social respects. The book plays important role to get modellers in the psychological, neurological, social or biomedical disciplines interested in AmI as a high-potential application area for their models. From the other side, the book may make researchers in Computer Science and Artificial and Ambient Intelligence more aware of the possibilities to incorporate more substantial knowledge from the psychological, neurological, social and biomedical disciplines in AmI architectures and applications.

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Diane J. Cook – författare

Mining Graph Data

Mining Graph Data

Activity Learning

Activity Learning

Activity Learning

Advanced Methods for Knowledge Discovery from Complex Data

Advanced Methods for Knowledge Discovery from Complex Data

Advanced Methods for Knowledge Discovery from Complex Data

Human Behavior Understanding

Human Behavior Understanding

Human Aspects in Ambient Intelligence

Human Aspects in Ambient Intelligence

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Mina sidor

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