Utility-Based Learning from Data

Utility-Based Learning from Data
Author: Craig Friedman
Publisher: CRC Press
Total Pages: 418
Release: 2016-04-19
Genre: Business & Economics
ISBN: 1420011286

Utility-Based Learning from Data provides a pedagogical, self-contained discussion of probability estimation methods via a coherent approach from the viewpoint of a decision maker who acts in an uncertain environment. This approach is motivated by the idea that probabilistic models are usually not learned for their own sake; rather, they are used t

Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities

Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities
Author: Bhatt, Chintan
Publisher: IGI Global
Total Pages: 180
Release: 2019-08-02
Genre: Education
ISBN: 1799800121

Modern education has increased its reach through ICT tools and techniques. To manage educational data with the help of modern artificial intelligence, data and web mining techniques on dedicated cloud or grid platforms for educational institutes can be used. By utilizing data science techniques to manage educational data, the safekeeping, delivery, and use of knowledge can be increased for better quality education. Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities is a critical scholarly resource that explores data mining and management techniques that promote the improvement and optimization of educational data systems. The book intends to provide new models, platforms, tools, and protocols in data science for educational data analysis and introduces innovative hybrid system models dedicated to data science. Including topics such as automatic assessment, educational analytics, and machine learning, this book is essential for IT specialists, data analysts, computer engineers, education professionals, administrators, policymakers, researchers, academicians, and technology experts.

Thinking With Data

Thinking With Data
Author: Marsha C. Lovett
Publisher: Psychology Press
Total Pages: 488
Release: 2012-08-21
Genre: Psychology
ISBN: 1136679421

The chapters in Thinking With Data are based on presentations given at the 33rd Carnegie Symposium on Cognition. The Symposium was motivated by the confluence of three emerging trends: (1) the increasing need for people to think effectively with data at work, at school, and in everyday life, (2) the expanding technologies available to support people as they think with data, and (3) the growing scientific interest in understanding how people think with data. What is thinking with data? It is the set of cognitive processes used to identify, integrate, and communicate the information present in complex numerical, categorical, and graphical data. This book offers a multidisciplinary presentation of recent research on the topic. Contributors represent a variety of disciplines: cognitive and developmental psychology; math, science, and statistics education; and decision science. The methods applied in various chapters similarly reflect a scientific diversity, including qualitative and quantitative analysis, experimentation and classroom observation, computational modeling, and neuroimaging. Throughout the book, research results are presented in a way that connects with both learning theory and instructional application. The book is organized in three sections: Part I focuses on the concepts of uncertainty and variation and on how people understand these ideas in a variety of contexts. Part II focuses on how people work with data to understand its structure and draw conclusions from data either in terms of formal statistical analyses or informal assessments of evidence. Part III focuses on how people learn from data and how they use data to make decisions in daily and professional life.

Handbook on Decision Support Systems 2

Handbook on Decision Support Systems 2
Author: Frada Burstein
Publisher: Springer Science & Business Media
Total Pages: 827
Release: 2008-01-22
Genre: Business & Economics
ISBN: 3540487166

As the most comprehensive reference work dealing with decision support systems (DSS), this book is essential for the library of every DSS practitioner, researcher, and educator. Written by an international array of DSS luminaries, it contains more than 70 chapters that approach decision support systems from a wide variety of perspectives. These range from classic foundations to cutting-edge thought, informative to provocative, theoretical to practical, historical to futuristic, human to technological, and operational to strategic. The chapters are conveniently organized into ten major sections that novices and experts alike will refer to for years to come.

Discovery Science

Discovery Science
Author: Larisa Soldatova
Publisher: Springer
Total Pages: 487
Release: 2018-10-22
Genre: Computers
ISBN: 3030017710

This book constitutes the proceedings of the 21st International Conference on Discovery Science, DS 2018, held in Limassol, Cyprus, in October 2018, co-located with the International Symposium on Methodologies for Intelligent Systems, ISMIS 2018. The 30 full papers presented together with 5 abstracts of invited talks in this volume were carefully reviewed and selected from 71 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in the following topical sections: Classification; meta-learning; reinforcement learning; streams and time series; subgroup and subgraph discovery; text mining; and applications.

Collaborative Filtering Using Data Mining and Analysis

Collaborative Filtering Using Data Mining and Analysis
Author: Bhatnagar, Vishal
Publisher: IGI Global
Total Pages: 336
Release: 2016-07-13
Genre: Computers
ISBN: 1522504907

Internet usage has become a normal and essential aspect of everyday life. Due to the immense amount of information available on the web, it has become obligatory to find ways to sift through and categorize the overload of data while removing redundant material. Collaborative Filtering Using Data Mining and Analysis evaluates the latest patterns and trending topics in the utilization of data mining tools and filtering practices. Featuring emergent research and optimization techniques in the areas of opinion mining, text mining, and sentiment analysis, as well as their various applications, this book is an essential reference source for researchers and engineers interested in collaborative filtering.

Knowledge Discovery in Databases: PKDD 2007

Knowledge Discovery in Databases: PKDD 2007
Author: Joost N. Kok
Publisher: Springer
Total Pages: 660
Release: 2007-08-30
Genre: Computers
ISBN: 3540749764

This book constitutes the refereed proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007, held in Warsaw, Poland, co-located with ECML 2007, the 18th European Conference on Machine Learning. The 28 revised full papers and 35 revised short papers present original results on leading-edge subjects of knowledge discovery from conventional and complex data and address all current issues in the area.

Preference Learning

Preference Learning
Author: Johannes Fürnkranz
Publisher: Springer Science & Business Media
Total Pages: 457
Release: 2010-11-19
Genre: Computers
ISBN: 3642141250

The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. And, generalizing beyond training data, models thus learned may be used for preference prediction. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.

Big Data Quantification for Complex Decision-Making

Big Data Quantification for Complex Decision-Making
Author: Zhang, Chao
Publisher: IGI Global
Total Pages: 328
Release: 2024-04-16
Genre: Business & Economics
ISBN:

Many professionals are facing a monumental challenge: navigating the intricate landscape of information to make impactful choices. The sheer volume and complexity of big data have ushered in a shift, demanding innovative methodologies and frameworks. Big Data Quantification for Complex Decision-Making tackles this challenge head-on, offering a comprehensive exploration of the tools necessary to distill valuable insights from datasets. This book serves as a tool for professionals, researchers, and students, empowering them to not only comprehend the significance of big data in decision-making but also to translate this understanding into real-world decision making. The central objective of the book is to examine the relationship between big data and decision-making. It strives to address multiple objectives, including understanding the intricacies of big data in decision-making, navigating methodological nuances, managing uncertainty adeptly, and bridging theoretical foundations with real-world applications. The book's core aspiration is to provide readers with a comprehensive toolbox, seamlessly integrating theoretical frameworks, practical applications, and forward-thinking perspectives. This equips readers with the means to effectively navigate the data-rich landscape of modern decision-making, fostering a heightened comprehension of strategic big data utilization. Tailored for a diverse audience, this book caters to researchers and academics in data science, decision science, machine learning, artificial intelligence, and related domains.

Big Data Analytics

Big Data Analytics
Author: Naveen Kumar
Publisher: Springer
Total Pages: 274
Release: 2015-11-24
Genre: Computers
ISBN: 3319270575

This book constitutes the refereed conference proceedings of the Fourth International Conference on Big Data Analytics, BDA 2015, held in Hyderabad, India, in December 2015. The 9 revised full papers and 9 invited papers were carefully reviewed and selected from 61 submissions and cover topics on big data: security and privacy; big data in commerce; big data: models and algorithms; and big data in medicine.