875,408 research outputs found
Statistical topological data analysis using persistence landscapes
We define a new topological summary for data that we call the persistence
landscape. Since this summary lies in a vector space, it is easy to combine
with tools from statistics and machine learning, in contrast to the standard
topological summaries. Viewed as a random variable with values in a Banach
space, this summary obeys a strong law of large numbers and a central limit
theorem. We show how a number of standard statistical tests can be used for
statistical inference using this summary. We also prove that this summary is
stable and that it can be used to provide lower bounds for the bottleneck and
Wasserstein distances.Comment: 26 pages, final version, to appear in Journal of Machine Learning
Research, includes two additional examples not in the journal version: random
geometric complexes and Erdos-Renyi random clique complexe
Editorial Note
I am delighted to introduce the 2nd issue of volume 2 of the International Journal of Automation, Artificial Intelligence and Machine Learning (IJAAIML). Artificial Intelligence (AI) and Machine Learning (ML) have applications in wide range of domains such as finance, national security, health care, criminal justice, transportation, and smart cities. With the focus on new ideas related to artificial intelligence and machine learning, this journal offers a platform to the authors in academia and industry to publish their novel research. It aims to serve the scientific community with brand new research publications to advance the research in AI and ML
Online Clustering of Bandits
We introduce a novel algorithmic approach to content recommendation based on
adaptive clustering of exploration-exploitation ("bandit") strategies. We
provide a sharp regret analysis of this algorithm in a standard stochastic
noise setting, demonstrate its scalability properties, and prove its
effectiveness on a number of artificial and real-world datasets. Our
experiments show a significant increase in prediction performance over
state-of-the-art methods for bandit problems.Comment: In E. Xing and T. Jebara (Eds.), Proceedings of 31st International
Conference on Machine Learning, Journal of Machine Learning Research Workshop
and Conference Proceedings, Vol.32 (JMLR W&CP-32), Beijing, China, Jun.
21-26, 2014 (ICML 2014), Submitted by Shuai Li
(https://sites.google.com/site/shuailidotsli
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