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    Metacognition: Fundaments, Applications, and Trends: A Profile of the Current State-Of-The-Art
    Recent Advances in Ensembles for Feature Selection
    Fuzzy Cognitive Maps for Applied Sciences and Engineering: From Fundamentals to Extensions and Learning Algorithms
    Ebook series4 titles

    Intelligent Systems Reference Library Series

    By Minhua Ma, Alejandro Peña-Ayala, Margarita N. Favorskaya and

    ()

    About this series

    This research-oriented book presents key contributions on architecting the digital transformation. It includes the following main sections covering 20 chapters:

     

    ·        Digital Transformation

     

    ·        Digital Business

     

    ·        Digital Architecture

     

    ·        Decision Support

     

    ·        Digital Applications

      

    Focusing on digital architectures for smart digital products and services, it is a valuable resource for researchers, doctoral students, postgraduates, graduates, undergraduates, academics and practitioners interested in digital transformation.


    LanguageEnglish
    PublisherSpringer
    Release dateDec 2, 2013
    Metacognition: Fundaments, Applications, and Trends: A Profile of the Current State-Of-The-Art
    Recent Advances in Ensembles for Feature Selection
    Fuzzy Cognitive Maps for Applied Sciences and Engineering: From Fundamentals to Extensions and Learning Algorithms

    Titles in the series (4)

    • 54

      Fuzzy Cognitive Maps for Applied Sciences and Engineering: From Fundamentals to Extensions and Learning Algorithms
      Fuzzy Cognitive Maps for Applied Sciences and Engineering: From Fundamentals to Extensions and Learning Algorithms

      Fuzzy Cognitive Maps for Applied Sciences and Engineering: From Fundamentals to Extensions and Learning Algorithms

      Elpiniki I. Papageorgiou
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      Fuzzy Cognitive Maps (FCM) constitute cognitive models in the form of fuzzy directed graphs consisting of two basic elements: the nodes, which basically correspond to “concepts” bearing different states of activation depending on the knowledge they represent, and the “edges” denoting the causal effects that each source node exercises on the receiving concept expressed through weights. Weights take values in the interval [-1,1], which denotes the positive, negative or neutral causal relationship between two concepts. An FCM can be typically obtained through linguistic terms, inherent to fuzzy systems, but with a structure similar to the neural networks, which facilitates data processing, and has capabilities for training and adaptation. During the last 10 years, an exponential growth of published papers in FCMs was followed showing great impact potential. Different FCM structures and learning schemes have been developed, while numerous studies report their use in many contexts with highly successful modeling results.   The aim of this book is to fill the existing gap in the literature concerning fundamentals, models, extensions and learning algorithms for FCMs in knowledge engineering. It comprehensively covers the state-of-the-art FCM modeling and learning methods, with algorithms, codes and software tools, and provides a set of applications that demonstrate their various usages in applied sciences and engineering.

      Fuzzy Cognitive Maps (FCM) constitute cognitive models in the form of fuzzy directed graphs consisting of two basic elements: the nodes, which basically correspond to “concepts” bearing different states of activation depending on the knowledge they represent, and the “edges” denoting the causal effects that each source node exercises on the receiving concept expressed through weights. Weights take values in the interval [-1,1], which denotes the positive, negative or neutral causal relationship between two concepts. An FCM can be typically obtained through linguistic terms, inherent to fuzzy systems, but with a structure similar to the neural networks, which facilitates data processing, and has capabilities for training and adaptation. During the last 10 years, an exponential growth of published papers in FCMs was followed showing great impact potential. Different FCM structures and learning schemes have been developed, while numerous studies report their use in many contexts with highly successful modeling results.   The aim of this book is to fill the existing gap in the literature concerning fundamentals, models, extensions and learning algorithms for FCMs in knowledge engineering. It comprehensively covers the state-of-the-art FCM modeling and learning methods, with algorithms, codes and software tools, and provides a set of applications that demonstrate their various usages in applied sciences and engineering.

    • 76

      Metacognition: Fundaments, Applications, and Trends: A Profile of the Current State-Of-The-Art
      Metacognition: Fundaments, Applications, and Trends: A Profile of the Current State-Of-The-Art

      Metacognition: Fundaments, Applications, and Trends: A Profile of the Current State-Of-The-Art

      Alejandro Peña-Ayala
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      This book is devoted to the Metacognition arena. It highlights works that show relevant analysis, reviews, theoretical, and methodological proposals, as well as studies, approaches, applications, and tools that shape current state, define trends and inspire future research. As a result of the revision process fourteen manuscripts were accepted and organized into five parts as follows: · Conceptual: contains conceptual works oriented to: (1) review models of strategy instruction and tailor a hybrid strategy; (2) unveil second-order judgments and define a method to assess metacognitive judgments; (3) introduces a conceptual model to describe the metacognitive activity as an autopoietic system. · Framework: offers three works concerned with: (4) stimulate metacognitive skills and self-regulatory functions; (5) evaluate metacognitive skills and self-regulated learning at problem solving; (6) deal with executive management metacognition and strategic knowledge metacognition. · Studies: reports research related to: (7) uncover how metacognitive awareness of listening strategies bias listening proficiency; (8) unveil how metacognitive skills and motivation are achieved in science informal learning; (9) tackle stress at learning by means of coping strategies. · Approaches: focus on the following targets: (10) social metacognition to support collaborative problem solving; (11) metacognitive skills to be stimulated in computer supported collaborative learning; (12) metacognitive knowledge and metacognitive experiences are essential for teaching practices. · Tools: promotes the use of intelligent tutoring systems such as: (13) BioWorld allows learners to practice medical diagnostic by providing virtual patient cases; (14) MetaHistoReasoning provides examples to learners and inquiries about the causes of historical events. This volume will be a source of interest for researchers, practitioners, professors, and postgraduate students aimed at updating their knowledge and finding targets for future work in the metacognition arena.

      This book is devoted to the Metacognition arena. It highlights works that show relevant analysis, reviews, theoretical, and methodological proposals, as well as studies, approaches, applications, and tools that shape current state, define trends and inspire future research. As a result of the revision process fourteen manuscripts were accepted and organized into five parts as follows: · Conceptual: contains conceptual works oriented to: (1) review models of strategy instruction and tailor a hybrid strategy; (2) unveil second-order judgments and define a method to assess metacognitive judgments; (3) introduces a conceptual model to describe the metacognitive activity as an autopoietic system. · Framework: offers three works concerned with: (4) stimulate metacognitive skills and self-regulatory functions; (5) evaluate metacognitive skills and self-regulated learning at problem solving; (6) deal with executive management metacognition and strategic knowledge metacognition. · Studies: reports research related to: (7) uncover how metacognitive awareness of listening strategies bias listening proficiency; (8) unveil how metacognitive skills and motivation are achieved in science informal learning; (9) tackle stress at learning by means of coping strategies. · Approaches: focus on the following targets: (10) social metacognition to support collaborative problem solving; (11) metacognitive skills to be stimulated in computer supported collaborative learning; (12) metacognitive knowledge and metacognitive experiences are essential for teaching practices. · Tools: promotes the use of intelligent tutoring systems such as: (13) BioWorld allows learners to practice medical diagnostic by providing virtual patient cases; (14) MetaHistoReasoning provides examples to learners and inquiries about the causes of historical events. This volume will be a source of interest for researchers, practitioners, professors, and postgraduate students aimed at updating their knowledge and finding targets for future work in the metacognition arena.

    • 147

      Recent Advances in Ensembles for Feature Selection
      Recent Advances in Ensembles for Feature Selection

      Recent Advances in Ensembles for Feature Selection

      Verónica Bolón-Canedo
      0 ratings
      0 ratings

      This book offers a comprehensive overview of ensemble learning in the field of feature selection (FS), which consists of combining the output of multiple methods to obtain better results than any single method. It reviews various techniques for combining partial results, measuring diversity and evaluating ensemble performance. With the advent of Big Data, feature selection (FS) has become more necessary than ever to achieve dimensionality reduction. With so many methods available, it is difficult to choose the most appropriate one for a given setting, thus making the ensemble paradigm an interesting alternative. The authors first focus on the foundations of ensemble learning and classical approaches, before diving into the specific aspects of ensembles for FS, such as combining partial results, measuring diversity and evaluating ensemble performance. Lastly, the book shows examples of successful applications of ensembles for FS and introduces the new challenges thatresearchers now face. As such, the book offers a valuable guide for all practitioners, researchers and graduate students in the areas of machine learning and data mining. 

      This book offers a comprehensive overview of ensemble learning in the field of feature selection (FS), which consists of combining the output of multiple methods to obtain better results than any single method. It reviews various techniques for combining partial results, measuring diversity and evaluating ensemble performance. With the advent of Big Data, feature selection (FS) has become more necessary than ever to achieve dimensionality reduction. With so many methods available, it is difficult to choose the most appropriate one for a given setting, thus making the ensemble paradigm an interesting alternative. The authors first focus on the foundations of ensemble learning and classical approaches, before diving into the specific aspects of ensembles for FS, such as combining partial results, measuring diversity and evaluating ensemble performance. Lastly, the book shows examples of successful applications of ensembles for FS and introduces the new challenges thatresearchers now face. As such, the book offers a valuable guide for all practitioners, researchers and graduate students in the areas of machine learning and data mining. 

    • 175

      Computer Vision in Advanced Control Systems-5: Advanced Decisions in Technical and Medical Applications
      Computer Vision in Advanced Control Systems-5: Advanced Decisions in Technical and Medical Applications

      Computer Vision in Advanced Control Systems-5: Advanced Decisions in Technical and Medical Applications

      Margarita N. Favorskaya
      0 ratings
      0 ratings

      This book applies novel theories to improve algorithms in complex data analysis in various fields, including object detection, remote sensing, data transmission, data fusion, gesture recognition, and medical image processing and analysis. It is intended for Ph.D. students, academics, researchers, and software developers working in the areas of digital video processing and computer vision technologies.  

      This book applies novel theories to improve algorithms in complex data analysis in various fields, including object detection, remote sensing, data transmission, data fusion, gesture recognition, and medical image processing and analysis. It is intended for Ph.D. students, academics, researchers, and software developers working in the areas of digital video processing and computer vision technologies.  

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