ohbm openscienceroom2019

Demo: Kernel methods for machine learning applications · Issue #1 · ohbm/OpenScienceRoom2019


This library fills an important void in the ever-growing python-based machine learning ecosystem, where users are limited to few predefined kernels without the ability to customize or extend them for their own applications. This library defines the KernelMatrix class that is central to all the kernel methods. As it is a key bridge between input data and kernel learning algorithms, it is designed to be highly usable and extensible to different applications and data types. Kernel operations implemented are normalization, centering, product, alignment, linear combination and ranking. Convenience classes, such as Kernel{Set,Bucket}, are designed for easy management of a large collection of kernels. Dealing with diverse kernels and their fusion is necessary for automatic kernel selection in applications such as Multiple Kernel Learning. Besides numerical kernels, we designed this library to provide categorical, string and graph kernels, with the same attractive properties of intuitive and highly-testable API. Besides non-numerical kernels, we aim to provide a deeply extensible framework for arbitrary input data types, such as sequences and trees, via pyradigm. Moreover, drop-in Estimator classes are provided for seamless usage in scikit-learn ecosystem.



The official Twitter hashtag set of the Open Science Room for 2019 is #OHBM2019 #OSR. Use it to coordinate events, meetings and discussions! Scroll down to see details of the Oral sessions and demos, Lightning talks and Scheduled meetings in the Open Science Room. How are journals, granting agencies, and consortia working alongside the open science movement? This session will explore recently introduced policy changes from each of these groups, focusing on their motivations and the potential impact on the academic ecosystem.