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Determinantal Point Processes for Machine Learning by Ben Taskar, Alex Kulesza (Paperback, 2012)

About this product

About this product

Product Information

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory. In contrast to traditional structured models like Markov random fields, which become intractable and hard to approximate in the presence of negative correlations, DPPs offer efficient and exact algorithms for sampling, marginalization, conditioning, and other inference tasks. While they have been studied extensively by mathematicians, giving rise to a deep and beautiful theory, DPPs are relatively new in machine learning. This book provides a comprehensible introduction to DPPs, focusing on the intuitions, algorithms, and extensions that are most relevant to the machine learning community, and shows how DPPs can be applied to real-world applications like finding diverse sets of high-quality search results, building informative summaries by selecting diverse sentences from documents, modeling non-overlapping human poses in images or video, and automatically building timelines of important news stories. It presents the general mathematical background to DPPs along with a range of modeling extensions, efficient algorithms, and theoretical results that aim to enable practical modeling and learning.

Product Identifiers

Publisher
Now Publishers Inc
ISBN-13
9781601986283
eBay Product ID (ePID)
140053438

Product Key Features

Author
Ben Taskar, Alex Kulesza
Publication Name
Determinantal Point Processes for Machine Learning
Format
Paperback
Language
English
Subject
Computer Science
Publication Year
2012
Type
Textbook
Number of Pages
178 Pages

Dimensions

Item Height
234mm
Item Width
156mm
Item Weight
259g

Additional Product Features

Title_Author
Alex Kulesza, Ben Taskar
Series Title
Foundations and Trends (R) in Machine Learning
Country/Region of Manufacture
United States

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