Determinantal Point Processes for Machine Learning 2024 pdf epub mobi 电子书


Determinantal Point Processes for Machine Learning

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发表于2024-12-23

Determinantal Point Processes for Machine Learning 2024 pdf epub mobi 电子书

Determinantal Point Processes for Machine Learning 2024 pdf epub mobi 电子书

Determinantal Point Processes for Machine Learning 2024 pdf epub mobi 电子书



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出版者:Now Publishers Inc
作者:Alex Kulesza
出品人:
页数:178
译者:
出版时间:2012
价格:0
装帧:平装
isbn号码:9781601986283
丛书系列:Foundations and Trends® in Machine Learning

图书标签: Machine_Learning   


Determinantal Point Processes for Machine Learning 2024 pdf epub mobi 电子书 图书描述

Alex Kulesza and Ben Taskar (2012), "Determinantal Point Processes for Machine Learning", Foundations and Trends® in Machine Learning: Vol. 5: No. 2–3, pp 123-286. http://dx.doi.org/10.1561/2200000044

https://www.nowpublishers.com/article/Details/MAL-044

http://www.alexkulesza.com/pubs/dpps_fnt12.pdf

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. Determinantal Point Processes for Machine Learning 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.

Determinantal Point Processes for Machine Learning 2024 pdf epub mobi 电子书

Determinantal Point Processes for Machine Learning 2024 pdf epub mobi 电子书
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