Meta Learning In Computational Intelligence

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Meta-Learning in Computational Intelligence

Norbert Jankowski,Włodzisław Duch,Krzysztof Grąbczewski
Meta Learning in Computational Intelligence Book PDF

Author : Norbert Jankowski,Włodzisław Duch,Krzysztof Grąbczewski
Publisher : Springer
Page : 359 pages
File Size : 49,9 Mb
Release : 2011-06-10
Category : Technology & Engineering
ISBN : 9783642209802

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Meta-Learning in Computational Intelligence by Norbert Jankowski,Włodzisław Duch,Krzysztof Grąbczewski Pdf

Computational Intelligence (CI) community has developed hundreds of algorithms for intelligent data analysis, but still many hard problems in computer vision, signal processing or text and multimedia understanding, problems that require deep learning techniques, are open. Modern data mining packages contain numerous modules for data acquisition, pre-processing, feature selection and construction, instance selection, classification, association and approximation methods, optimization techniques, pattern discovery, clusterization, visualization and post-processing. A large data mining package allows for billions of ways in which these modules can be combined. No human expert can claim to explore and understand all possibilities in the knowledge discovery process. This is where algorithms that learn how to learnl come to rescue. Operating in the space of all available data transformations and optimization techniques these algorithms use meta-knowledge about learning processes automatically extracted from experience of solving diverse problems. Inferences about transformations useful in different contexts help to construct learning algorithms that can uncover various aspects of knowledge hidden in the data. Meta-learning shifts the focus of the whole CI field from individual learning algorithms to the higher level of learning how to learn. This book defines and reveals new theoretical and practical trends in meta-learning, inspiring the readers to further research in this exciting field.

Meta-Learning

Lan Zou
Meta Learning Book PDF

Author : Lan Zou
Publisher : Elsevier
Page : 404 pages
File Size : 47,6 Mb
Release : 2022-11-05
Category : Computers
ISBN : 9780323903707

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Meta-Learning by Lan Zou Pdf

Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve. Meta-Learning: Theory, Algorithms and Applications shows how meta-learning in combination with DNNs advances towards AGI. Meta-Learning: Theory, Algorithms and Applications explains the fundamentals of meta-learning by providing answers to these questions: What is meta-learning?; why do we need meta-learning?; how are self-improved meta-learning mechanisms heading for AGI ?; how can we use meta-learning in our approach to specific scenarios? The book presents the background of seven mainstream paradigms: meta-learning, few-shot learning, deep learning, transfer learning, machine learning, probabilistic modeling, and Bayesian inference. It then explains important state-of-the-art mechanisms and their variants for meta-learning, including memory-augmented neural networks, meta-networks, convolutional Siamese neural networks, matching networks, prototypical networks, relation networks, LSTM meta-learning, model-agnostic meta-learning, and the Reptile algorithm. The book takes a deep dive into nearly 200 state-of-the-art meta-learning algorithms from top tier conferences (e.g. NeurIPS, ICML, CVPR, ACL, ICLR, KDD). It systematically investigates 39 categories of tasks from 11 real-world application fields: Computer Vision, Natural Language Processing, Meta-Reinforcement Learning, Healthcare, Finance and Economy, Construction Materials, Graphic Neural Networks, Program Synthesis, Smart City, Recommended Systems, and Climate Science. Each application field concludes by looking at future trends or by giving a summary of available resources. Meta-Learning: Theory, Algorithms and Applications is a great resource to understand the principles of meta-learning and to learn state-of-the-art meta-learning algorithms, giving the student, researcher and industry professional the ability to apply meta-learning for various novel applications. A comprehensive overview of state-of-the-art meta-learning techniques and methods associated with deep neural networks together with a broad range of application areas Coverage of nearly 200 state-of-the-art meta-learning algorithms, which are promoted by premier global AI conferences and journals, and 300 to 450 pieces of key research Systematic and detailed exploration of the most crucial state-of-the-art meta-learning algorithm mechanisms: model-based, metric-based, and optimization-based Provides solutions to the limitations of using deep learning and/or machine learning methods, particularly with small sample sizes and unlabeled data Gives an understanding of how meta-learning acts as a stepping stone to Artificial General Intelligence in 39 categories of tasks from 11 real-world application fields

Metalearning

Pavel Brazdil,Christophe Giraud Carrier,Carlos Soares,Ricardo Vilalta
Metalearning Book PDF

Author : Pavel Brazdil,Christophe Giraud Carrier,Carlos Soares,Ricardo Vilalta
Publisher : Springer Science & Business Media
Page : 182 pages
File Size : 51,8 Mb
Release : 2008-11-26
Category : Computers
ISBN : 9783540732624

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Metalearning by Pavel Brazdil,Christophe Giraud Carrier,Carlos Soares,Ricardo Vilalta Pdf

Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.

Automated Machine Learning and Meta-Learning for Multimedia

Wenwu Zhu,Xin Wang
Automated Machine Learning and Meta Learning for Multimedia Book PDF

Author : Wenwu Zhu,Xin Wang
Publisher : Springer Nature
Page : 240 pages
File Size : 40,8 Mb
Release : 2022-01-01
Category : Computers
ISBN : 9783030881320

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Automated Machine Learning and Meta-Learning for Multimedia by Wenwu Zhu,Xin Wang Pdf

This book disseminates and promotes the recent research progress and frontier development on AutoML and meta-learning as well as their applications on computer vision, natural language processing, multimedia and data mining related fields. These are exciting and fast-growing research directions in the general field of machine learning. The authors advocate novel, high-quality research findings, and innovative solutions to the challenging problems in AutoML and meta-learning. This topic is at the core of the scope of artificial intelligence, and is attractive to audience from both academia and industry. This book is highly accessible to the whole machine learning community, including: researchers, students and practitioners who are interested in AutoML, meta-learning, and their applications in multimedia, computer vision, natural language processing and data mining related tasks. The book is self-contained and designed for introductory and intermediate audiences. No special prerequisite knowledge is required to read this book.

Meta-Learning in Decision Tree Induction

Krzysztof Grąbczewski
Meta Learning in Decision Tree Induction Book PDF

Author : Krzysztof Grąbczewski
Publisher : Springer
Page : 343 pages
File Size : 49,6 Mb
Release : 2013-09-11
Category : Technology & Engineering
ISBN : 9783319009605

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Meta-Learning in Decision Tree Induction by Krzysztof Grąbczewski Pdf

The book focuses on different variants of decision tree induction but also describes the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimental methodology and evaluation framework is provided. Meta-learning is discussed in great detail in the second half of the book. The exposition starts by presenting a comprehensive review of many meta-learning approaches explored in the past described in literature, including for instance approaches that provide a ranking of algorithms. The approach described can be related to other work that exploits planning whose aim is to construct data mining workflows. The book stimulates interchange of ideas between different, albeit related, approaches.

Meta-Learning in Computational Intelligence

Norbert Jankowski,Włodzisław Duch,Krzysztof Grąbczewski
Meta Learning in Computational Intelligence Book PDF

Author : Norbert Jankowski,Włodzisław Duch,Krzysztof Grąbczewski
Publisher : Springer Science & Business Media
Page : 362 pages
File Size : 45,8 Mb
Release : 2011-06-10
Category : Computers
ISBN : 9783642209796

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Meta-Learning in Computational Intelligence by Norbert Jankowski,Włodzisław Duch,Krzysztof Grąbczewski Pdf

Computational Intelligence (CI) community has developed hundreds of algorithms for intelligent data analysis, but still many hard problems in computer vision, signal processing or text and multimedia understanding, problems that require deep learning techniques, are open. Modern data mining packages contain numerous modules for data acquisition, pre-processing, feature selection and construction, instance selection, classification, association and approximation methods, optimization techniques, pattern discovery, clusterization, visualization and post-processing. A large data mining package allows for billions of ways in which these modules can be combined. No human expert can claim to explore and understand all possibilities in the knowledge discovery process. This is where algorithms that learn how to learnl come to rescue. Operating in the space of all available data transformations and optimization techniques these algorithms use meta-knowledge about learning processes automatically extracted from experience of solving diverse problems. Inferences about transformations useful in different contexts help to construct learning algorithms that can uncover various aspects of knowledge hidden in the data. Meta-learning shifts the focus of the whole CI field from individual learning algorithms to the higher level of learning how to learn. This book defines and reveals new theoretical and practical trends in meta-learning, inspiring the readers to further research in this exciting field.

Automated Machine Learning

Frank Hutter,Lars Kotthoff,Joaquin Vanschoren
Automated Machine Learning Book PDF

Author : Frank Hutter,Lars Kotthoff,Joaquin Vanschoren
Publisher : Springer
Page : 223 pages
File Size : 55,5 Mb
Release : 2019-05-17
Category : Computers
ISBN : 9783030053185

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Automated Machine Learning by Frank Hutter,Lars Kotthoff,Joaquin Vanschoren Pdf

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Meta-Learning Frameworks for Imaging Applications

Sharma, Ashok,Sengar, Sandeep Singh,Singh, Parveen
Meta Learning Frameworks for Imaging Applications Book PDF

Author : Sharma, Ashok,Sengar, Sandeep Singh,Singh, Parveen
Publisher : IGI Global
Page : 271 pages
File Size : 45,9 Mb
Release : 2023-09-28
Category : Computers
ISBN : 9781668476611

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Meta-Learning Frameworks for Imaging Applications by Sharma, Ashok,Sengar, Sandeep Singh,Singh, Parveen Pdf

Meta-learning, or learning to learn, has been gaining popularity in recent years to adapt to new tasks systematically and efficiently in machine learning. In the book, Meta-Learning Frameworks for Imaging Applications, experts from the fields of machine learning and imaging come together to explore the current state of meta-learning and its application to medical imaging and health informatics. The book presents an overview of the meta-learning framework, including common versions such as model-agnostic learning, memory augmentation, prototype networks, and learning to optimize. It also discusses how meta-learning can be applied to address fundamental limitations of deep neural networks, such as high data demand, computationally expensive training, and limited ability for task transfer. One critical topic in imaging is image segmentation, and the book explores how a meta-learning-based framework can help identify the best image segmentation algorithm, which would be particularly beneficial in the healthcare domain. This book is relevant to healthcare institutes, e-commerce companies, and educational institutions, as well as professionals and practitioners in the intelligent system, computational data science, network applications, and biomedical applications fields. It is also useful for domain developers and project managers from diagnostic and pharmacy companies involved in the development of medical expert systems. Additionally, graduate and master students in intelligent systems, big data management, computational intelligent approaches, computer vision, and biomedical science can use this book for their final projects and specific courses.

Hands-On Meta Learning with Python

Sudharsan Ravichandiran
Hands On Meta Learning with Python Book PDF

Author : Sudharsan Ravichandiran
Publisher : Packt Publishing Ltd
Page : 218 pages
File Size : 41,5 Mb
Release : 2018-12-31
Category : Computers
ISBN : 9781789537024

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Hands-On Meta Learning with Python by Sudharsan Ravichandiran Pdf

Explore a diverse set of meta-learning algorithms and techniques to enable human-like cognition for your machine learning models using various Python frameworks Key FeaturesUnderstand the foundations of meta learning algorithmsExplore practical examples to explore various one-shot learning algorithms with its applications in TensorFlowMaster state of the art meta learning algorithms like MAML, reptile, meta SGDBook Description Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster. Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning. By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models. What you will learnUnderstand the basics of meta learning methods, algorithms, and typesBuild voice and face recognition models using a siamese networkLearn the prototypical network along with its variantsBuild relation networks and matching networks from scratchImplement MAML and Reptile algorithms from scratch in PythonWork through imitation learning and adversarial meta learningExplore task agnostic meta learning and deep meta learningWho this book is for Hands-On Meta Learning with Python is for machine learning enthusiasts, AI researchers, and data scientists who want to explore meta learning as an advanced approach for training machine learning models. Working knowledge of machine learning concepts and Python programming is necessary.

Learning to Learn

Sebastian Thrun,Lorien Pratt
Learning to Learn Book PDF

Author : Sebastian Thrun,Lorien Pratt
Publisher : Springer Science & Business Media
Page : 346 pages
File Size : 44,9 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461555292

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Learning to Learn by Sebastian Thrun,Lorien Pratt Pdf

Over the past three decades or so, research on machine learning and data mining has led to a wide variety of algorithms that learn general functions from experience. As machine learning is maturing, it has begun to make the successful transition from academic research to various practical applications. Generic techniques such as decision trees and artificial neural networks, for example, are now being used in various commercial and industrial applications. Learning to Learn is an exciting new research direction within machine learning. Similar to traditional machine-learning algorithms, the methods described in Learning to Learn induce general functions from experience. However, the book investigates algorithms that can change the way they generalize, i.e., practice the task of learning itself, and improve on it. To illustrate the utility of learning to learn, it is worthwhile comparing machine learning with human learning. Humans encounter a continual stream of learning tasks. They do not just learn concepts or motor skills, they also learn bias, i.e., they learn how to generalize. As a result, humans are often able to generalize correctly from extremely few examples - often just a single example suffices to teach us a new thing. A deeper understanding of computer programs that improve their ability to learn can have a large practical impact on the field of machine learning and beyond. In recent years, the field has made significant progress towards a theory of learning to learn along with practical new algorithms, some of which led to impressive results in real-world applications. Learning to Learn provides a survey of some of the most exciting new research approaches, written by leading researchers in the field. Its objective is to investigate the utility and feasibility of computer programs that can learn how to learn, both from a practical and a theoretical point of view.

Foundations of Computational Intelligence

Aboul-Ella Hassanien,Ajith Abraham,Athanasios V. Vasilakos,Witold Pedrycz
Foundations of Computational Intelligence Book PDF

Author : Aboul-Ella Hassanien,Ajith Abraham,Athanasios V. Vasilakos,Witold Pedrycz
Publisher : Springer Science & Business Media
Page : 401 pages
File Size : 43,5 Mb
Release : 2009-05-05
Category : Computers
ISBN : 9783642010811

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Foundations of Computational Intelligence by Aboul-Ella Hassanien,Ajith Abraham,Athanasios V. Vasilakos,Witold Pedrycz Pdf

Recent years have seen numerous applications across a variety of fields using various techniques of Computational Intelligence. This book, one of a series on the foundations of Computational Intelligence, is focused on learning and approximation.

AI 2016: Advances in Artificial Intelligence

Byeong Ho Kang,Quan Bai
AI 2016 Advances in Artificial Intelligence Book PDF

Author : Byeong Ho Kang,Quan Bai
Publisher : Springer
Page : 731 pages
File Size : 41,5 Mb
Release : 2016-11-25
Category : Computers
ISBN : 9783319501277

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AI 2016: Advances in Artificial Intelligence by Byeong Ho Kang,Quan Bai Pdf

This book constitutes the refereed proceedings of the 29th Australasian Joint Conference on Artificial Intelligence, AI 2016, held in Hobart, TAS, Australia, in December 2016. The 40 full papers and 18 short papers presented together with 8 invited short papers were carefully reviewed and selected from 121 submissions. The papers are organized in topical sections on agents and multiagent systems; AI applications and innovations; big data; constraint satisfaction, search and optimisation; knowledge representation and reasoning; machine learning and data mining; social intelligence; and text mining and NLP. The proceedings also contains 2 contributions of the AI 2016 doctoral consortium and 6 contributions of the SMA 2016.

Applications of Computational Intelligence in Data-Driven Trading

Cris Doloc
Applications of Computational Intelligence in Data Driven Trading Book PDF

Author : Cris Doloc
Publisher : John Wiley & Sons
Page : 304 pages
File Size : 40,8 Mb
Release : 2019-10-29
Category : Business & Economics
ISBN : 9781119550501

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Applications of Computational Intelligence in Data-Driven Trading by Cris Doloc Pdf

“Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence.” – Prof. Terrence J. Sejnowski, Computational Neurobiologist The main objective of this book is to create awareness about both the promises and the formidable challenges that the era of Data-Driven Decision-Making and Machine Learning are confronted with, and especially about how these new developments may influence the future of the financial industry. The subject of Financial Machine Learning has attracted a lot of interest recently, specifically because it represents one of the most challenging problem spaces for the applicability of Machine Learning. The author has used a novel approach to introduce the reader to this topic: The first half of the book is a readable and coherent introduction to two modern topics that are not generally considered together: the data-driven paradigm and Computational Intelligence. The second half of the book illustrates a set of Case Studies that are contemporarily relevant to quantitative trading practitioners who are dealing with problems such as trade execution optimization, price dynamics forecast, portfolio management, market making, derivatives valuation, risk, and compliance. The main purpose of this book is pedagogical in nature, and it is specifically aimed at defining an adequate level of engineering and scientific clarity when it comes to the usage of the term “Artificial Intelligence,” especially as it relates to the financial industry. The message conveyed by this book is one of confidence in the possibilities offered by this new era of Data-Intensive Computation. This message is not grounded on the current hype surrounding the latest technologies, but on a deep analysis of their effectiveness and also on the author’s two decades of professional experience as a technologist, quant and academic.

Computational Intelligence and Security

Yue Hao,Jiming Liu,Yuping Wang,Yiu-ming Cheung,Hujun Yin,Licheng Jiao,Jianfeng Ma,Yong-Chang Jiao
Computational Intelligence and Security Book PDF

Author : Yue Hao,Jiming Liu,Yuping Wang,Yiu-ming Cheung,Hujun Yin,Licheng Jiao,Jianfeng Ma,Yong-Chang Jiao
Publisher : Springer
Page : 1122 pages
File Size : 55,5 Mb
Release : 2006-06-18
Category : Computers
ISBN : 9783540315995

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Computational Intelligence and Security by Yue Hao,Jiming Liu,Yuping Wang,Yiu-ming Cheung,Hujun Yin,Licheng Jiao,Jianfeng Ma,Yong-Chang Jiao Pdf

The two volume set LNAI 3801 and LNAI 3802 constitute the refereed proceedings of the annual International Conference on Computational Intelligence and Security, CIS 2005, held in Xi'an, China, in December 2005. The 338 revised papers presented - 254 regular and 84 extended papers - were carefully reviewed and selected from over 1800 submissions. The first volume is organized in topical sections on learning and fuzzy systems, evolutionary computation, intelligent agents and systems, intelligent information retrieval, support vector machines, swarm intelligence, data mining, pattern recognition, and applications. The second volume is subdivided in topical sections on cryptography and coding, cryptographic protocols, intrusion detection, security models and architecture, security management, watermarking and information hiding, web and network applications, image and signal processing, and applications.

Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems

Essam Halim Houssein,Mohamed Abd Elaziz,Diego Oliva,Laith Abualigah
Integrating Meta Heuristics and Machine Learning for Real World Optimization Problems Book PDF

Author : Essam Halim Houssein,Mohamed Abd Elaziz,Diego Oliva,Laith Abualigah
Publisher : Springer Nature
Page : 501 pages
File Size : 51,8 Mb
Release : 2022-06-04
Category : Technology & Engineering
ISBN : 9783030990794

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Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems by Essam Halim Houssein,Mohamed Abd Elaziz,Diego Oliva,Laith Abualigah Pdf

This book collects different methodologies that permit metaheuristics and machine learning to solve real-world problems. This book has exciting chapters that employ evolutionary and swarm optimization tools combined with machine learning techniques. The fields of applications are from distribution systems until medical diagnosis, and they are also included different surveys and literature reviews that will enrich the reader. Besides, cutting-edge methods such as neuroevolutionary and IoT implementations are presented in some chapters. In this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and can be used in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the material can be helpful for research from the evolutionary computation, artificial intelligence communities.