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The Art of Feature Engineering
When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.
The Art of Feature Engineering
A practical guide for data scientists who want to improve the performance of any machine learning solution with feature engineering.
The Semantic Web - ISWC 2006
This book constitutes the refereed proceedings of the 5th International Semantic Web Conference, ISWC 2006, held in Athens, GA, USA in November 2006. It features more than 52 papers that address all current issues in the field of the semantic Web, ranging from theoretical aspects to various applied topics. An additional 14 papers detail applications in government, public health, public service, academic, and industry.
Ensemble Methods for Machine Learning
In Ensemble Methods for Machine Learning you'll learn to implement the most important ensemble machine learning methods from scratch. Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real-world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
The Oxford Handbook of Computational Linguistics
Ruslan Mitkov's highly successful Oxford Handbook of Computational Linguistics has been substantially revised and expanded in this second edition. Alongside updated accounts of the topics covered in the first edition, it includes 17 new chapters on subjects such as semantic role-labelling, text-to-speech synthesis, translation technology, opinion mining and sentiment analysis, and the application of Natural Language Processing in educational and biomedical contexts, among many others. The volume is divided into four parts that examine, respectively: the linguistic fundamentals of computational linguistics; the methods and resources used, such as statistical modelling, machine learning, and corpus annotation; key language processing tasks including text segmentation, anaphora resolution, and speech recognition; and the major applications of Natural Language Processing, from machine translation to author profiling. The book will be an essential reference for researchers and students in computational linguistics and Natural Language Processing, as well as those working in related industries.
Pacific Symposium on Biocomputing 2003
The Pacific Symposium on Biocomputing (PSB 2003) is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. The rigorously peer-reviewed papers and presentations are collected in this archival proceedings volume. PSB 2003 brings together top researchers from the US, the Asia-Pacific region and around the world to exchange research findings and address open issues in all aspects of computational biology. PSB is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling and other computational methods, as applied t...
Neural Generation of Textual Summaries from Knowledge Base Triples
Most people need textual or visual interfaces to help them make sense of Semantic Web data. In this book, the author investigates the problems associated with generating natural language summaries for structured data encoded as triples using deep neural networks. An end-to-end trainable architecture is proposed, which encodes the information from a set of knowledge graph triples into a vector of fixed dimensionality, and generates a textual summary by conditioning the output on this encoded vector. Different methodologies for building the required data-to-text corpora are explored to train and evaluate the performance of the approach. Attention is first focused on generating biographies, and...
Natural Language Generation
The Third International Conference on Natural Language Generation (INLG 2004) was held from 14th to 16th July 2004 at Careys Manor, Brockenhurst, UK. Supported by the Association for Computational Linguistics Special - terest Group on Generation, the conference continued a twenty-year tradition of biennial international meetings on research into natural language generation. Recent conference venues have included Mitzpe Ramon, Israel (INLG 2000) and New York, USA (INLG 2002). It was our pleasure to invite the thriving and friendly NLG research community to the beautiful New Forest in the south of England for INLG 2004. INLG is the leading international conference in the ?eld of natural langua...
Final Jeopardy
The “charming and terrifying” story of IBM’s breakthrough in artificial intelligence, from the Business Week technology writer and author of The Numerati (Publishers Weekly, starred review). For centuries, people have dreamed of creating a machine that thinks like a human. Scientists have made progress: computers can now beat chess grandmasters and help prevent terrorist attacks. Yet we still await a machine that exhibits the rich complexity of human thought—one that doesn’t just crunch numbers, or take us to a relevant web page, but understands and communicates with us. With the creation of Watson, IBM’s Jeopardy!-playing computer, we are one step closer to that goal. In Final J...
Applied Data Science in Tourism
Access to large data sets has led to a paradigm shift in the tourism research landscape. Big data is enabling a new form of knowledge gain, while at the same time shaking the epistemological foundations and requiring new methods and analysis approaches. It allows for interdisciplinary cooperation between computer sciences and social and economic sciences, and complements the traditional research approaches. This book provides a broad basis for the practical application of data science approaches such as machine learning, text mining, social network analysis, and many more, which are essential for interdisciplinary tourism research. Each method is presented in principle, viewed analytically, ...
