The Experts below are selected from a list of 630072 Experts worldwide ranked by ideXlab platform
Parag Kulkarni - One of the best experts on this subject based on the ideXlab platform.
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Reverse Hypothesis Machine Learning - Reverse Hypothesis Machine Learning
Intelligent Systems Reference Library, 2017Co-Authors: Parag KulkarniAbstract:This book introduces a paradigm of reverse hypothesis Machines (RHM), focusing on knowledge innovation and Machine Learning. Knowledge- acquisition -based Learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based Learning is the need of time. Since under-Learning results in cognitive inabilities and over-Learning compromises freedom, there is need for optimal Machine Learning. All existing Learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same—the forward hypothesis Machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative Learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for Machine Learning researchers and professionals as well as Machine intelligence enthusiasts. It can also used by practitioners to develop new Machine Learning applications to solve problems that require creativity
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Reinforcement and Systemic Machine Learning for Decision Making - Introduction to Reinforcement and Systemic Machine Learning
Reinforcement and Systemic Machine Learning for Decision Making, 2012Co-Authors: Parag KulkarniAbstract:This chapter contains sections titled: Introduction Supervised, Unsupervised, and Semisupervised Machine Learning Traditional Learning Methods and History of Machine Learning What Is Machine Learning? Machine-Learning Problem Learning Paradigms Machine-Learning Techniques and Paradigms What Is Reinforcement Learning? Reinforcement Function and Environment Function Need of Reinforcement Learning Reinforcement Learning and Machine Intelligence What Is Systemic Learning? What Is Systemic Machine Learning? Challenges in Systemic Machine Learning Reinforcement Machine Learning and Systemic Machine Learning Case Study Problem Detection in a Vehicle Summary Reference ]]>
Padraig Cunningham - One of the best experts on this subject based on the ideXlab platform.
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Machine Learning Techniques for Multimedia
Machine Learning Techniques for Multimedia, 2016Co-Authors: Matthieu Cord, Padraig CunninghamAbstract:Processing multimedia content has emerged as a key area for the application of Machine Learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Applying Machine Learning techniques to multimedia content involves special considerations – the data is typically of very high dimension, and the normal distinction between supervised and unsupervised techniques does not always apply. This book provides a comprehensive coverage of the most important Machine Learning techniques used and their application in this domain. Arising from the EU MUSCLE network, a program that drew together multidisciplinary teams with expertise in Machine Learning, pattern recognition, artificial intelligence, and image, video, text and crossmedia processing, the book first introduces the Machine Learning principles and techniques that are applied in multimedia data processing and analysis. The second part focuses on multimedia data processing applications, with chapters examining specific Machine Learning issues in domains such as image retrieval, biometrics, semantic labelling, mobile devices, and mining in text and music. This book will be suitable for practitioners, researchers and students engaged with Machine Learning in multimedia applications.
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Machine Learning Techniques for Multimedia - Machine Learning Techniques for Multimedia
Cognitive Technologies, 2008Co-Authors: Matthieu Cord, Padraig CunninghamAbstract:International audienceProcessing multimedia content has emerged as a key area for the application of Machine Learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Applying Machine Learning techniques to multimedia content involves special considerations – the data is typically of very high dimension, and the normal distinction between supervised and unsupervised techniques does not always apply.This book provides a comprehensive coverage of the most important Machine Learning techniques used and their application in this domain. Arising from the EU MUSCLE network, a program that drew together multidisciplinary teams with expertise in Machine Learning, pattern recognition, artificial intelligence, and image, video, text and crossmedia processing, the book first introduces the Machine Learning principles and techniques that are applied in multimedia data processing and analysis. The second part focuses on multimedia data processing applications, with chapters examining specific Machine Learning issues in domains such as image retrieval, biometrics, semantic labelling, mobile devices, and mining in text and music.This book will be suitable for practitioners, researchers and students engaged with Machine Learning in multimedia applications
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Machine Learning Techniques for Multimedia - Machine Learning Techniques for Multimedia
Cognitive Technologies, 2008Co-Authors: Matthieu Cord, Padraig CunninghamAbstract:Processing multimedia content has emerged as a key area for the application of Machine Learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Applying Machine Learning techniques to multimedia content involves special considerations – the data is typically of very high dimension, and the normal distinction between supervised and unsupervised techniques does not always apply. This book provides a comprehensive coverage of the most important Machine Learning techniques used and their application in this domain. Arising from the EU MUSCLE network, a program that drew together multidisciplinary teams with expertise in Machine Learning, pattern recognition, artificial intelligence, and image, video, text and crossmedia processing, the book first introduces the Machine Learning principles and techniques that are applied in multimedia data processing and analysis. The second part focuses on multimedia data processing applications, with chapters examining specific Machine Learning issues in domains such as image retrieval, biometrics, semantic labelling, mobile devices, and mining in text and music. This book will be suitable for practitioners, researchers and students engaged with Machine Learning in multimedia applications.
Matthieu Cord - One of the best experts on this subject based on the ideXlab platform.
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Machine Learning Techniques for Multimedia
Machine Learning Techniques for Multimedia, 2016Co-Authors: Matthieu Cord, Padraig CunninghamAbstract:Processing multimedia content has emerged as a key area for the application of Machine Learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Applying Machine Learning techniques to multimedia content involves special considerations – the data is typically of very high dimension, and the normal distinction between supervised and unsupervised techniques does not always apply. This book provides a comprehensive coverage of the most important Machine Learning techniques used and their application in this domain. Arising from the EU MUSCLE network, a program that drew together multidisciplinary teams with expertise in Machine Learning, pattern recognition, artificial intelligence, and image, video, text and crossmedia processing, the book first introduces the Machine Learning principles and techniques that are applied in multimedia data processing and analysis. The second part focuses on multimedia data processing applications, with chapters examining specific Machine Learning issues in domains such as image retrieval, biometrics, semantic labelling, mobile devices, and mining in text and music. This book will be suitable for practitioners, researchers and students engaged with Machine Learning in multimedia applications.
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Machine Learning Techniques for Multimedia - Machine Learning Techniques for Multimedia
Cognitive Technologies, 2008Co-Authors: Matthieu Cord, Padraig CunninghamAbstract:International audienceProcessing multimedia content has emerged as a key area for the application of Machine Learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Applying Machine Learning techniques to multimedia content involves special considerations – the data is typically of very high dimension, and the normal distinction between supervised and unsupervised techniques does not always apply.This book provides a comprehensive coverage of the most important Machine Learning techniques used and their application in this domain. Arising from the EU MUSCLE network, a program that drew together multidisciplinary teams with expertise in Machine Learning, pattern recognition, artificial intelligence, and image, video, text and crossmedia processing, the book first introduces the Machine Learning principles and techniques that are applied in multimedia data processing and analysis. The second part focuses on multimedia data processing applications, with chapters examining specific Machine Learning issues in domains such as image retrieval, biometrics, semantic labelling, mobile devices, and mining in text and music.This book will be suitable for practitioners, researchers and students engaged with Machine Learning in multimedia applications
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Machine Learning Techniques for Multimedia - Machine Learning Techniques for Multimedia
Cognitive Technologies, 2008Co-Authors: Matthieu Cord, Padraig CunninghamAbstract:Processing multimedia content has emerged as a key area for the application of Machine Learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Applying Machine Learning techniques to multimedia content involves special considerations – the data is typically of very high dimension, and the normal distinction between supervised and unsupervised techniques does not always apply. This book provides a comprehensive coverage of the most important Machine Learning techniques used and their application in this domain. Arising from the EU MUSCLE network, a program that drew together multidisciplinary teams with expertise in Machine Learning, pattern recognition, artificial intelligence, and image, video, text and crossmedia processing, the book first introduces the Machine Learning principles and techniques that are applied in multimedia data processing and analysis. The second part focuses on multimedia data processing applications, with chapters examining specific Machine Learning issues in domains such as image retrieval, biometrics, semantic labelling, mobile devices, and mining in text and music. This book will be suitable for practitioners, researchers and students engaged with Machine Learning in multimedia applications.
Zhiyuan Chen - One of the best experts on this subject based on the ideXlab platform.
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Lifelong Machine Learning
2016Co-Authors: Zhiyuan ChenAbstract:Abstract NOTE ⁃ A New Edition of This Title is Available: Lifelong Machine Learning, Second Edition Lifelong Machine Learning (or Lifelong Learning) is an advanced Machine Learning paradigm that le...
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Lifelong Machine Learning
2016Co-Authors: Zhiyuan ChenAbstract:Lifelong Machine Learning (or Lifelong Learning) is an advanced Machine Learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future Learning. In the process, the learner becomes more and more knowledgeable and effective at Learning. This Learning ability is one of the hallmarks of human intelligence. However, the current dominant Machine Learning paradigm learns in isolation: given a training dataset, it runs a Machine Learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future Learning. Although this isolated Learning paradigm has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Lifelong Learning aims to achieve this capability. As statistical Machine Learning matures, it is time to make a major effort to break the isolated Learning tradition and to study lifelong Learning to bring Machine Learning to new heights. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong Learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong Learning.
Evelyne Lutton - One of the best experts on this subject based on the ideXlab platform.
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Human and Machine Learning - Evaluation of Interactive Machine Learning Systems
Human and Machine Learning, 2018Co-Authors: Nadia Boukhelifa, Anastasia Bezerianos, Evelyne LuttonAbstract:The evaluation of interactive Machine Learning systems remains a difficult task. These systems learn from and adapt to the human, but at the same time, the human receives feedback and adapts to the system. Getting a clear understanding of these subtle mechanisms of cooperation and co-adaptation is challenging. In this chapter, we report on our experience in designing and evaluating various interactive Machine Learning applications from different domains. We argue for coupling two types of validation: algorithm-centered analysis, to study the computational behaviour of the system; and human-centered evaluation, to observe the utility and effectiveness of the application for end-users. We use a visual analytics application for guided search, built using an interactive evolutionary approach, as an exemplar of our work. We argue that human-centered design and evaluation complement al-gorithmic analysis, and can play an important role in addressing the " black-box " effect of Machine Learning. Finally, we discuss research opportunities that require human-computer interaction methodologies, in order to support both the visible and hidden roles that humans play in interactive Machine Learning.
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Human and Machine Learning - Evaluation of Interactive Machine Learning Systems
Human and Machine Learning, 2018Co-Authors: Nadia Boukhelifa, Anastasia Bezerianos, Evelyne LuttonAbstract:The evaluation of interactive Machine Learning systems remains a difficult task. These systems learn from and adapt to the human, but at the same time, the human receives feedback and adapts to the system. Getting a clear understanding of these subtle mechanisms of co-operation and co-adaptation is challenging. In this chapter, we report on our experience in designing and evaluating various interactive Machine Learning applications from different domains. We argue for coupling two types of validation: algorithm-centred analysis, to study the computational behaviour of the system; and human-centred evaluation, to observe the utility and effectiveness of the application for end-users. We use a visual analytics application for guided search, built using an interactive evolutionary approach, as an exemplar of our work. Our observation is that human-centred design and evaluation complement algorithmic analysis, and can play an important role in addressing the “black-box” effect of Machine Learning. Finally, we discuss research opportunities that require human-computer interaction methodologies, in order to support both the visible and hidden roles that humans play in interactive Machine Learning.
