5 research outputs found
Accurate, Fast and Scalable Kernel Ridge Regression on Parallel and Distributed Systems
We propose two new methods to address the weak scaling problems of KRR: the
Balanced KRR (BKRR) and K-means KRR (KKRR). These methods consider alternative
ways to partition the input dataset into p different parts, generating p
different models, and then selecting the best model among them. Compared to a
conventional implementation, KKRR2 (optimized version of KKRR) improves the
weak scaling efficiency from 0.32% to 38% and achieves a 591times speedup for
getting the same accuracy by using the same data and the same hardware (1536
processors). BKRR2 (optimized version of BKRR) achieves a higher accuracy than
the current fastest method using less training time for a variety of datasets.
For the applications requiring only approximate solutions, BKRR2 improves the
weak scaling efficiency to 92% and achieves 3505 times speedup (theoretical
speedup: 4096 times).Comment: This paper has been accepted by ACM International Conference on
Supercomputing (ICS) 201
Predicting the Possibilistic Score of OWL Axioms through Modified Support Vector Clustering
International audienceWe address the problem of predicting a score for candidate axioms within the context of ontology learning. The prediction is based on a learning procedure based on support vector clustering originally developed for inferring the membership functions of fuzzy sets, and on a similarity measure for subsumption axioms based on semantic considerations and reminiscent of the Jaccard index. We show that the proposed method successfully learns the possibilistic score in a knowledge base consisting of pairs of candidate OWL axioms, meanwhile highlighting that a small subset of the considered axioms turns out harder to learn than the remainder