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- Morgan & Claypool Publishers 2016; US$ 44.00
This book covers the topic of temporal tagging, the detection of temporal expressions and the normalization of their semantics to some standard format. It places a special focus on the challenges and opportunities of domain-sensitive temporal tagging. After providing background knowledge on the concept of time, the book continues with a comprehensive... more...
- Morgan & Claypool Publishers 2016; US$ 56.00
This unique book provides a comprehensive introduction to the most popular syntax-based statistical machine translation models, filling a gap in the current literature for researchers and developers in human language technologies. While phrase-based models have previously dominated the field, syntax-based approaches have proved a popular alternative,... more...
- Packt Publishing 2017; US$ 71.99
Scala will be a valuable tool to have on hand during your data science journey for everything from data cleaning to cutting-edge machine learning About This Book Build data science and data engineering solutions with ease An in-depth look at each stage of the data analysis process ? from reading and collecting data to distributed analytics ... more...
- Morgan & Claypool Publishers 2017; US$ 31.96
A lot of digital ink has been spilled on "big data" over the past few years. Most of this surge owes its origin to the various types of unstructured data in the wild, among which the proliferation of text-heavy data is particularly overwhelming, attributed to the daily use of web documents, business reviews, news, social posts, etc., by so many people... more...
- O'Reilly Media 2017; US$ 42.99
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two... more...
- O'Reilly Media 2012; US$ 31.99
Create your own natural language training corpus for machine learning. Whether you?re working with English, Chinese, or any other natural language, this hands-on book guides you through a proven annotation development cycle?the process of adding metadata to your training corpus to help ML algorithms work more efficiently. You don?t need any programming... more...
- O'Reilly Media 2014; US$ 25.99
Learn how to apply test-driven development (TDD) to machine-learning algorithms?and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and... more...
- Springer Berlin Heidelberg 2007; US$ 64.36
Edited in collaboration with FoLLI, the Association of Logic, Language and Information, this book constitutes the third volume of the FoLLI LNAI subline. The 17 revised papers of this Festschrift volume - published in honour of Daniele Mundici on the occasion of his 60th birthday - include invited extended versions of the most interesting contributions... more...