T-norms driven loss functions for machine learning

Classifying Legal Norms with Active Machine Learning

Topic-Driven Crawlers: Machine Learning Issues

Topic driven crawlers are increasingly seen as a way to address the scalability limitations of universal search engines, by distributing the crawling process across users, queries, or even client computers. The context available to such crawlers can guide the navigation of links with the goal of efficiently locating highly relevant target pages. We developed a framework to fairly evaluate topic...

Thoughtful Machine Learning - A Test-Driven Approach

Spend your time even for only few minutes to read a book. Reading a book will never reduce and waste your time to be useless. Reading, for some people become a need that is to do every day such as spending time for eating. Now, what about you? Do you like to read a book? Now, we will show you a new book enPDFd thoughtful machine learning a test driven approach that can be a new way to explore t...

Author Disambiguation Using Error-driven Machine Learning with a Ranking Loss Function

Author disambiguation is the problem of determining whether records in a publications database refer to the same person. A common supervised machine learning approach is to build a classifier to predict whether a pair of records is coreferent, followed by a clustering step to enforce transitivity. However, this approach ignores powerful evidence obtainable by examining sets (rather than pairs) ...

Machine Learning Based Profile Driven Scheduling Algorithm

Clusters of commodity servers are increasingly the platform of choice for running computationally and IO intensive jobs in a variety of industries. It is expected that using clusters will reduce the average job response time. But improper submission of jobs to clusters may lead to two problems, first it leads to blocking of jobs (waiting for results from other jobs) second it leads to disturbin...