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Relational Machine Learning Algorithms.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Relational Machine Learning Algorithms./
作者:	Samadianzakaria, Alireza.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	146 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
Contained By:	Dissertations Abstracts International83-06B.
標題:	Applied mathematics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28845169
ISBN:	9798759923510

Relational Machine Learning Algorithms.
Samadianzakaria, Alireza.

Relational Machine Learning Algorithms. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 146 p.

Source: Dissertations Abstracts International, Volume: 83-06, Section: B.

Thesis (Ph.D.)--University of Pittsburgh, 2021.

This item must not be sold to any third party vendors.

The majority of learning tasks faced by data scientists involve relational data, yet most standard algorithms for standard learning problems are not designed to accept relational data as input. The standard practice to address this issue is to join the relational data to create the type of geometric input that standard learning algorithms expect. Unfortunately, this standard practice has exponential worst-case time and space complexity. This leads us to consider what we call the Relational Learning Question: "Which standard learning algorithms can be efficiently implemented on relational data, and for those that cannot, is there an alternative algorithm that can be efficiently implemented on relational data and that has similar performance guarantees to the standard algorithm?"In this dissertation, we address the relational learning question for the well-known problems of support vector machine (SVM), logistic regression, and $k$-means clustering. First, we design an efficient relational algorithm for regularized linear SVM and logistic regression using sampling methods. We show how to implement a variation of gradient descent that provides a nearly optimal approximation guarantee for stable instances. For the $k$-means problem, we show that the $k$-means++ algorithm can be efficiently implemented on relational data, and that a slight variation of adaptive k-means algorithm can be efficiently implemented on relational data while maintaining a constant approximation guarantee. On the way to developing these algorithms, we give an efficient approximation algorithm for certain sum-product queries with additive inequalities that commonly arise.

ISBN: 9798759923510Subjects--Topical Terms:

2122814
Applied mathematics.

Relational Machine Learning Algorithms.
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