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{ "category": "SciPy 2013", "language": "English", "slug": "using-python-for-structured-prediction-scipy-201-1", "speakers": [], "tags": [ "Tech" ], "id": 2123, "state": 1, "title": "Using Python for Structured Prediction; SciPy 2013 Presentation", "summary": "Authors: Zinkov, Rob\n\nTrack: Machine Learning\n\nMany machine learning problems involved datasets with complex dependencies between variables we are trying to predict and even the data points themselves. Unfortunately most machine learning libraries are unable to model these dependencies and make use of them. In this talk, I will introduce two libraries pyCRFsuite and pyStruct and show how they can be used to solve machine learning problems where modeling the relations between data points is crucial for getting reasonable accuracy. I will cover how these libraries can be used for classifying webpages as spam, named entity extraction, and sentiment analysis.", "description": "", "quality_notes": "", "copyright_text": "http://www.youtube.com/t/terms", "embed": "<object width=\"640\" height=\"390\"><param name=\"movie\" value=\"http://youtube.com/v/ItA3K7i2Pa0?version=3&amp;hl=en_US\"></param><param name=\"allowFullScreen\" value=\"true\"></param><param name=\"allowscriptaccess\" value=\"always\"></param><embed src=\"http://youtube.com/v/ItA3K7i2Pa0?version=3&amp;hl=en_US\" type=\"application/x-shockwave-flash\" width=\"640\" height=\"390\" allowscriptaccess=\"always\" allowfullscreen=\"true\"></embed></object>", "thumbnail_url": "http://i1.ytimg.com/vi/ItA3K7i2Pa0/hqdefault.jpg", "duration": null, "video_ogv_length": null, "video_ogv_url": null, "video_ogv_download_only": false, "video_mp4_length": null, "video_mp4_url": null, "video_mp4_download_only": false, "video_webm_length": null, "video_webm_url": null, "video_webm_download_only": false, "video_flv_length": null, "video_flv_url": null, "video_flv_download_only": false, "source_url": "http://www.youtube.com/watch?v=ItA3K7i2Pa0", "whiteboard": "needs editing", "recorded": "2013-07-01", "added": "2013-07-04T10:08:57", "updated": "2014-04-08T20:28:26.452" }