The WebGenre Blog: The power of genre applied to digital information. By Marina Santini » Entries tagged with "machine learning"

Book Review: Fundamentals of Predictive Text Mining 2nd Ed. (2015)

Book Review: Fundamentals of Predictive Text Mining 2nd Ed. (2015)

Book Review: Weiss S. M., Indurkhya N. and Zhang T. (2015). Fundamentals of Predictive Text Mining. Springer-Verlag, London. Second Edition Informer website Winter 2016 Issue, Book Review The volume “Fundamentals of Predictive Text Mining”, 2nd ed. has nine chapters, a table of contents, a list of references, a Subject Index and an Author Index. The book also includes a Preface written by the three authors, Summary Abbriavions: ML=Machine Learning; NLP=Natural Language Processing; IR= Information Retrieval 1) In Chapter 1, “Overview of … Read entire article »

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Lecture 1: What is Machine Learning (ML4LT 2015)

Lecture 1: What is Machine Learning (ML4LT 2015)

Opening lecture to the Machine Learning for Language Technology courseat Uppsala University, Sweden. Autumn 2015. Lecture 1: What is Machine Learning? from Marina Santini … Read entire article »

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Lecture 2: Basic Concepts in Machine Learning for Language Technology

Machine Learning for Language Technology 2014 – Course Schedule … Read entire article »

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Lecture 7: Learning from Massive Datasets

Lecture 7: Learning from Massive Datasets from Marina Santini In this lecture we explore how big datasets can be used with the Weka workbench and what other issues are currently under discussion in the real world, for ex: big data applications, predictive linguistic analysis, new platforms and new programming languages. … Read entire article »

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Lecture 6: Ensemble Methods

Lecture 6: Ensemble Methods from Marina Santini What is an “ensemble learner”? How can we combine different base learners into an ensemble in order to improve the overall classification performance? In this lecture, these questions are addressed. … Read entire article »

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Lecture 5: Structured Prediction

Structured prediction or structured learning refers to supervised machine learning techniques that involve predicting structured objects, rather than single labels or real values. For example, the problem of translating a natural language sentence into a syntactic representation such as a parse tree can be seen as a structured prediction problem in which the structured output domain is the set of all possible parse trees. … Read entire article »

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Lecture 1: Introduction – Machine Learning for Language Technology

What Is Machine Learning? Machine learning is programming computers to optimize a performance criterion using example data or past experience. We have a model defined up to some parameters, and learning is the execution of a computer program to optimize the parameters of the model using the training data or past experience. The model may be predictive to make predictions in the future, or descriptive to gain knowledge from data, or both. Machine learning uses the theory of statistics in building mathematical models, because the core task is making inference from a sample. (Alpaydin, 2010) In this lecture, we discuss supervised learning starting from the simplest case. We introduce the concepts of: Margin, Noise, and Bias. … Read entire article »

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Reblogging: Practical advice for machine learning

Practical advice for machine learning: bias, variance and what to do next By Mikael Huss at Follow the data (http://followthedata.wordpress.com/about/) The online machine learning course given by Andrew Ng in 2011 (available here among many other places, including YouTube) is highly recommended in its entirety, but I just wanted to highlight a specific part of it, namely the “Practical advice part”, which touches on things that are not always included in machine learning and data mining courses, like “Deciding what do to do next” (the title of this lecture) or “debugging a learning algorithm” (the title of the first slide in that talk). His advice here focuses on the concepts of the bias and variance in statistical learning. I had been vaguely aware of the concepts of “bias and variance tradeoff” and “bias/variance decomposition” for a long time, but I had always … Read entire article »

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Book Outline: Automatic Identification of Genre in Web Pages (2011)

Automatic Identification of Genre in Web Pages: A new perspective [Paperback] Marina Santini (Author) Paperback: 332 pages Publisher: LAP LAMBERT Academic Publishing (December 19, 2011) Language: English ISBN-10: 3847306871 ISBN-13: 978-3847306870 Book Overview This book is divided into five parts: a preliminary part (Part I), three empirical parts (Parts II, III and IV) and an epilogue (Part V). … Read entire article »

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