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Book Description
This open access book provides a basic introduction to feature modelling and analysis as well as to the integration of AI methods with feature modelling. It is intended as an introduction for researchers and practitioners who are new to the field and will also serve as a state-of-the-art reference to this audience. While focusing on the AI perspective, the book covers the topics of feature modelling (including languages and semantics), feature model analysis, and interacting with feature model configurators. These topics are discussed along the AI areas of knowledge representation and reasoning, explainable AI, and machine learning.
Book Description
This open access book provides a basic introduction to feature modelling and analysis as well as to the integration of AI methods with feature modelling. It is intended as an introduction for researchers and practitioners who are new to the field and will also serve as a state-of-the-art reference to this audience. While focusing on the AI perspective, the book covers the topics of feature modelling (including languages and semantics), feature model analysis, and interacting with feature model configurators. These topics are discussed along the AI areas of knowledge representation and reasoning, explainable AI, and machine learning.
Author: Max Kuhn Publisher: CRC Press ISBN: 1351609467 Category : Business & Economics Languages : en Pages : 266
Book Description
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
Author: Christoph Molnar Publisher: Lulu.com ISBN: 0244768528 Category : Artificial intelligence Languages : en Pages : 320
Book Description
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Author: Alice Zheng Publisher: "O'Reilly Media, Inc." ISBN: 1491953195 Category : Computers Languages : en Pages : 218
Book Description
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques
Author: Sandeep Uttamchandani Publisher: "O'Reilly Media, Inc." ISBN: 1492075205 Category : Computers Languages : en Pages : 297
Book Description
Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can’t scale data science teams fast enough to keep up with the growing amounts of data to transform. What’s the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work. Build a self-service portal to support data discovery, quality, lineage, and governance Select the best approach for each self-service capability using open source cloud technologies Tailor self-service for the people, processes, and technology maturity of your data platform Implement capabilities to democratize data and reduce time to insight Scale your self-service portal to support a large number of users within your organization
Author: Sinan Ozdemir Publisher: Simon and Schuster ISBN: 1638351406 Category : Computers Languages : en Pages : 270
Book Description
Deliver huge improvements to your machine learning pipelines without spending hours fine-tuning parameters! This book’s practical case-studies reveal feature engineering techniques that upgrade your data wrangling—and your ML results. In Feature Engineering Bookcamp you will learn how to: Identify and implement feature transformations for your data Build powerful machine learning pipelines with unstructured data like text and images Quantify and minimize bias in machine learning pipelines at the data level Use feature stores to build real-time feature engineering pipelines Enhance existing machine learning pipelines by manipulating the input data Use state-of-the-art deep learning models to extract hidden patterns in data Feature Engineering Bookcamp guides you through a collection of projects that give you hands-on practice with core feature engineering techniques. You’ll work with feature engineering practices that speed up the time it takes to process data and deliver real improvements in your model’s performance. This instantly-useful book skips the abstract mathematical theory and minutely-detailed formulas; instead you’ll learn through interesting code-driven case studies, including tweet classification, COVID detection, recidivism prediction, stock price movement detection, and more. About the technology Get better output from machine learning pipelines by improving your training data! Use feature engineering, a machine learning technique for designing relevant input variables based on your existing data, to simplify training and enhance model performance. While fine-tuning hyperparameters or tweaking models may give you a minor performance bump, feature engineering delivers dramatic improvements by transforming your data pipeline. About the book Feature Engineering Bookcamp walks you through six hands-on projects where you’ll learn to upgrade your training data using feature engineering. Each chapter explores a new code-driven case study, taken from real-world industries like finance and healthcare. You’ll practice cleaning and transforming data, mitigating bias, and more. The book is full of performance-enhancing tips for all major ML subdomains—from natural language processing to time-series analysis. What's inside Identify and implement feature transformations Build machine learning pipelines with unstructured data Quantify and minimize bias in ML pipelines Use feature stores to build real-time feature engineering pipelines Enhance existing pipelines by manipulating input data About the reader For experienced machine learning engineers familiar with Python. About the author Sinan Ozdemir is the founder and CTO of Shiba, a former lecturer of Data Science at Johns Hopkins University, and the author of multiple textbooks on data science and machine learning. Table of Contents 1 Introduction to feature engineering 2 The basics of feature engineering 3 Healthcare: Diagnosing COVID-19 4 Bias and fairness: Modeling recidivism 5 Natural language processing: Classifying social media sentiment 6 Computer vision: Object recognition 7 Time series analysis: Day trading with machine learning 8 Feature stores 9 Putting it all together
Author: Frank Buschmann Publisher: Springer ISBN: 3540259341 Category : Computers Languages : en Pages : 216
Book Description
This volume represents the seventh edition of the ECOOP Workshop Reader, a compendiumofworkshopreportsfromthe17thEuropeanConferenceonObject- Oriented Programming (ECOOP 2003), held in Darmstadt, Germany, during July 21–25, 2003. The workshops were held during the ?rst two days of the conference. They cover a wide range of interesting and innovative topics in object-oriented te- nology and o?ered the participants an opportunity for interaction and lively discussion. Twenty-one workshops were selected from a total of 24 submissions based on their scienti?c merit, the actuality of the topic, and their potential for a lively interaction. Unfortunately, one workshop had to be cancelled. Special thanks are due to the workshop organizers who recorded and s- marized the discussions. We would also like to thank all the participants for their presentations and lively contributions to the discussion: they made this volume possible. Last, but not least, we wish to express our appreciation to the members of the organizing committee who put in countless hours setting up and coordinating the workshops. We hope that this snapshot of current object-oriented technology will prove stimulating to you. October 2003 Frank Buschmann Alejandro Buchmann Mariano Cilia Organization ECOOP 2003 was organized by the Software Technology Group, Department of Computer Science, Darmstadt University of Technology under the auspices of AITO (Association Internationale pour les Technologies Objets) in cooperation with ACM SIGPLAN. The proceedings of the main conference were published as LNCS 2743.
Author: Patrick Donohoe Publisher: Springer Science & Business Media ISBN: 1461543398 Category : Computers Languages : en Pages : 527
Book Description
Software product lines are emerging as a critical new paradigm for software development. Product lines are enabling organizations to achieve impressive time-to-market gains and cost reductions. With the increasing number of product lines and product-line researchers and practitioners, the time is right for a comprehensive examination of the issues surrounding the software product line approach. The Software Engineering Institute at Carnegie Mellon University is proud to sponsor the first conference on this important subject. This book comprises the proceedings of the First Software Product Line Conference (SPLC1), held August 28-31, 2000, in Denver, Colorado, USA. The twenty-seven papers of the conference technical program present research results and experience reports that cover all aspects of software product lines. Topics include business issues, enabling technologies, organizational issues, and life-cycle issues. Emphasis is placed on experiences in the development and fielding of product lines of complex systems, especially those that expose problems in the design, development, or evolution of software product lines. The book will be essential reading for researchers and practitioners alike.
Author: Andreas Tolk Publisher: Springer Science & Business Media ISBN: 3540880747 Category : Mathematics Languages : en Pages : 272
Book Description
The tremendous growth in the availability of inexpensive computing power and easy availability of computers have generated tremendous interest in the design and imp- mentation of Complex Systems. Computer-based solutions offer great support in the design of Complex Systems. Furthermore, Complex Systems are becoming incre- ingly complex themselves. This research book comprises a selection of state-of-the-art contributions to topics dealing with Complex Systems in a Knowledge-based En- ronment. Complex systems are ubiquitous. Examples comprise, but are not limited to System of Systems, Service-oriented Approaches, Agent-based Systems, and Complex Distributed Virtual Systems. These are application domains that require knowledge of engineering and management methods and are beyond the scope of traditional systems. The chapters in this book deal with a selection of topics which range from unc- tainty representation, management and the use of ontological means which support and are large-scale business integration. All contributions were invited and are based on the recognition of the expertise of the contributing authors in the field. By colle- ing these sources together in one volume, the intention was to present a variety of tools to the reader to assist in both study and work. The second intention was to show how the different facets presented in the chapters are complementary and contribute towards this emerging discipline designed to aid in the analysis of complex systems.
Author: Armin Haller Publisher: Springer ISBN: 3642383335 Category : Computers Languages : en Pages : 363
Book Description
This book constitutes the revised selected papers of the combined workshops on Web Information Systems Engineering, WISE 2011 and WISE 2012, held in Sydney, Australia, in October 2011 and in Paphos, Cyprus, in November 2012. The seven workshops of WISE 2011-2012 have reported the recent developments and advances in the contemporary topics in the related fields of: Advanced Reasoning Technology for e-Science (ART 2012), Cloud-Enabled Business Process Management (CeBPM 2012), Engineering in the Semantic Enterprise (ESE 2012), Social Web Analysis for Trend Detection (SoWeTrend 2012), Big Data and Cloud (BDC 2012), Personalization in Cloud and Service Computing (PC-S 2011), and User-Focused Service Engineering, Consumption and Aggregation (USECA 2011).
Author: ALEXANDER. FALKNER FELFERNIG (ANDREAS. BENAVIDES, DAVID.)
Author: ALEXANDER. FALKNER FELFERNIG (ANDREAS. BENAVIDES, DAVID.)
Author: Max Kuhn
Author: Christoph Molnar
Author: Alice Zheng
Author: Sandeep Uttamchandani
Author: Sinan Ozdemir
Author: Frank Buschmann
Author: Patrick Donohoe
Author: Andreas Tolk
Author: Armin Haller