{"action":"opened","issue":{"url":"https://api.github.com/repos/ohbm/OpenScienceRoom2019/issues/1","repository_url":"https://api.github.com/repos/ohbm/OpenScienceRoom2019","labels_url":"https://api.github.com/repos/ohbm/OpenScienceRoom2019/issues/1/labels{/name}","comments_url":"https://api.github.com/repos/ohbm/OpenScienceRoom2019/issues/1/comments","events_url":"https://api.github.com/repos/ohbm/OpenScienceRoom2019/issues/1/events","html_url":"https://github.com/ohbm/OpenScienceRoom2019/issues/1","id":440077338,"node_id":"MDU6SXNzdWU0NDAwNzczMzg=","number":1,"title":"Demo: Kernel methods for machine learning applications","user":{"login":"raamana","id":3196357,"node_id":"MDQ6VXNlcjMxOTYzNTc=","avatar_url":"https://avatars2.githubusercontent.com/u/3196357?v=4","gravatar_id":"","url":"https://api.github.com/users/raamana","html_url":"https://github.com/raamana","followers_url":"https://api.github.com/users/raamana/followers","following_url":"https://api.github.com/users/raamana/following{/other_user}","gists_url":"https://api.github.com/users/raamana/gists{/gist_id}","starred_url":"https://api.github.com/users/raamana/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/raamana/subscriptions","organizations_url":"https://api.github.com/users/raamana/orgs","repos_url":"https://api.github.com/users/raamana/repos","events_url":"https://api.github.com/users/raamana/events{/privacy}","received_events_url":"https://api.github.com/users/raamana/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2019-05-03T14:40:31Z","updated_at":"2019-05-03T14:40:31Z","closed_at":null,"author_association":"NONE","body":"**Title**\r\nKernel methods library for machine learning applications\r\n\r\n**Presentor and Affiliation**\r\nPradeep Reddy Raamana, \r\nRotman Research Institute, Baycrest Health Sciences, \r\nUniversity of Toronto, Toronto, Canada.\r\n\r\n**Collaborators**\r\nTBD\r\n\r\n**Github Link (if applicable)**\r\ngithub.com/raamana/kernelmethods\r\n\r\n**Abstract (max. 200 words):**\r\nkernelmethods is a pure python library defining modular classes that provides basic kernel methods and an intuitive interface for advanced functionality such as composite and hyper kernels. 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.\r\n\r\n**Preferred Session**\r\nDemo: New advances in open neuroimaging methods\r\n\r\n**Additional Context**\r\nWith the ever-increasing diversity of datasets and diseases in neuroimaging research, having the ability to learn the kernel on one's own dataset/application (instead of ramming a pre-defined kernel that users can't inspect or customize) is crucial to optimize the performance of a machine learning technique."}}
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