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Author: Gisele L. Pappa
Publisher: Springer Science & Business Media
ISBN: 3642025412
Category : Computers
Languages : en
Pages : 198
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Book Description
Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.
Author: Gisele L. Pappa
Publisher: Springer Science & Business Media
ISBN: 3642025412
Category : Computers
Languages : en
Pages : 198
Get Book Here
Book Description
Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.
Author: Pavel Brazdil
Publisher: Springer Nature
ISBN: 3030670244
Category : Artificial intelligence
Languages : en
Pages : 349
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Book Description
This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence.
Author: Qingquan Song
Publisher: Simon and Schuster
ISBN: 1617298050
Category : Computers
Languages : en
Pages : 334
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Book Description
Automated Machine Learning in Action reveals how you can automate the burdensome elements of designing and tuning your machine learning systems. --
Author: D. Braha
Publisher: Springer Science & Business Media
ISBN: 1475749112
Category : Computers
Languages : en
Pages : 530
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Book Description
Data Mining for Design and Manufacturing: Methods and Applications is the first book that brings together research and applications for data mining within design and manufacturing. The aim of the book is 1) to clarify the integration of data mining in engineering design and manufacturing, 2) to present a wide range of domains to which data mining can be applied, 3) to demonstrate the essential need for symbiotic collaboration of expertise in design and manufacturing, data mining, and information technology, and 4) to illustrate how to overcome central problems in design and manufacturing environments. The book also presents formal tools required to extract valuable information from design and manufacturing data, and facilitates interdisciplinary problem solving for enhanced decision making. Audience: The book is aimed at both academic and practising audiences. It can serve as a reference or textbook for senior or graduate level students in Engineering, Computer, and Management Sciences who are interested in data mining technologies. The book will be useful for practitioners interested in utilizing data mining techniques in design and manufacturing as well as for computer software developers engaged in developing data mining tools.
Author: Valliappa Lakshmanan
Publisher: Springer Science & Business Media
ISBN: 9400740751
Category : Science
Languages : en
Pages : 328
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Book Description
The ability to create automated algorithms to process gridded spatial data is increasingly important as remotely sensed datasets increase in volume and frequency. Whether in business, social science, ecology, meteorology or urban planning, the ability to create automated applications to analyze and detect patterns in geospatial data is increasingly important. This book provides students with a foundation in topics of digital image processing and data mining as applied to geospatial datasets. The aim is for readers to be able to devise and implement automated techniques to extract information from spatial grids such as radar, satellite or high-resolution survey imagery.
Author: Nicholas Diakopoulos
Publisher: Harvard University Press
ISBN: 0674239318
Category : Language Arts & Disciplines
Languages : en
Pages : 304
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Book Description
From hidden connections in big data to bots spreading fake news, journalism is increasingly computer-generated. Nicholas Diakopoulos explains the present and future of a world in which algorithms have changed how the news is created, disseminated, and received, and he shows why journalists—and their values—are at little risk of being replaced.
Author: Frank Hutter
Publisher: Springer
ISBN: 3030053180
Category : Computers
Languages : en
Pages : 223
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Book Description
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.
Author: Anand Deshpande
Publisher: Packt Publishing Ltd
ISBN: 1788476018
Category : Computers
Languages : en
Pages : 371
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Book Description
Build next-generation Artificial Intelligence systems with Java Key Features Implement AI techniques to build smart applications using Deeplearning4j Perform big data analytics to derive quality insights using Spark MLlib Create self-learning systems using neural networks, NLP, and reinforcement learning Book Description In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems. What you will learn Manage Artificial Intelligence techniques for big data with Java Build smart systems to analyze data for enhanced customer experience Learn to use Artificial Intelligence frameworks for big data Understand complex problems with algorithms and Neuro-Fuzzy systems Design stratagems to leverage data using Machine Learning process Apply Deep Learning techniques to prepare data for modeling Construct models that learn from data using open source tools Analyze big data problems using scalable Machine Learning algorithms Who this book is for This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus.
Author: Bhavani Thuraisingham
Publisher: CRC Press
ISBN: 1420045911
Category : Computers
Languages : en
Pages : 272
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Book Description
Focusing on three applications of data mining, Design and Implementation of Data Mining Tools explains how to create and employ systems and tools for intrusion detection, Web page surfing prediction, and image classification. Mainly based on the authors' own research work, the book takes a practical approach to the subject.The first part of the boo
Author: Kunpeng Liu
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Feature engineering is one of the most important components in data mining and machine learning. One of the key thrusts in data mining is to answer: How should a low-dimensional geometry structure be extracted and reconstructed from high-dimensional data? To solve this issue, researchers proposed feature selection, PCA, sparsity regularization, factorization, embedding, and deep learning. However, existing techniques are limited in achieving full automation, globally optimal, and explainable explicitness. Can I address the automation, optimal, and explainability challenges in data geometry reconstruction? A low-dimensional data geometry structure is crucial for SciML methods (e.g., GP models), and the accuracy of these methods depends on how one can learn the data geometry structure from data or physics-based models. This dissertation will target the problem of automated identification of an optimal and explicit low-dimensional data geometry from high dimensional data. I will propose a novel principled self-optimizing data geometry reconstruction framework by viewing feature generation and selection from the lens of Reinforcement Learning (RL). I will show that reconstructing a low-dimensional data geometry (a.k.a., feature space) can be accomplished by an interactive nested feature generation and selection framework, where feature generation is to generate new meaningful and explicit features, feature selection is to subset redundant features to reduce dimensionality, and an optimized sequential structure of generations and selections will result into an optimized feature space for a downstream machine learning task. Finally, I will highlight that the search for such an optimized sequential structure can be generalized as an advanced cascading reinforcement learning system.
