Active Learning
The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "oracle" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivate
Carnegie Mellon Machine Learning Lunch Seminar
All talks will be held on Mondays at Noon. Subscribe to the mailing list below for the room location. Lunch and drinks provided! Volunteer to give a talk today! Subscribe to the ML Lunch seminar announcement mailing list. Registration is open only to CMU or UPitt email addresses. Abstract Gating is a key feature in modern neural networks including LSTMs, GRUs and sparsely-gated deep neural network
dlib C++ Library
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charg
PyML - machine learning in Python — PyML v0.7.3 documentation
PyML - machine learning in Python¶ PyML is an interactive object oriented framework for machine learning written in Python. PyML focuses on SVMs and other kernel methods. It is supported on Linux and Mac OS X. News¶ Added wrapper for liblinear in version 0.7.8. This allows extremely fast training of linear SVMs. Features¶ Classifiers: support vector machines, nearest neighbor, ridge regression Mul
)
Active learning (machine learning) - Wikipedia
Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources when necessary. [1][2][3] In statistics literature, it is
K-NEL - 機械学習の「朱鷺の杜Wiki」
統計科学のフロンティアに続く新シリーズとして企画された新シリーズ確率と情報の科学 の第1巻として出版される「カーネル多変量解析」のサポートページです． カーネル法によるデータ解析は今や生命情報科学やデータマイニングの分野では標準である．しかし文字列やグラフ解析など別分野に適応できるように自身で設計しようとすると容易ではない．本書は，カーネル法という多変量解析の底に流れる基本的な考え方を紹介して，読者自身が設計を行う際の道標になるような内容をめざす．(岩波書店紹介文より) ↑ 書名: カーネル多変量解析 ―― 非線形データ解析の新しい展開 シリーズ名: 甘利俊一、麻生英樹、伊庭幸人 編 シリーズ確率と情報の科学 (第I期) 著者: 赤穂昭太郎 体裁＝A5判・上製・カバー・240頁 定価 3,850円（本体 3,500円 + 税10%） 発売日 2008年11月27日 (第1刷) 2009年6
KDD Cup from Yahoo! Labs
Two Tracks Track1 is aimed at predicting scores that users gave to various items. Track2 requires separation of loved songs from other songs. Both Track1 and Track2 are open to all research groups in academia and industry. To learn more click here. Learn the rhythm, predict the musical scores People have been fascinated by music since the dawn of humanity. A wide variety of music genres and sty
David MacKay: Information Theory, Inference, and Learning Algorithms: The Book
Information Theory, Inference, and Learning Algorithms (Hardback, 640 pages, Published September 2003) Order your copy Price: £35.00 / $60.00 from |CUP UK/USA| |amazon.co.uk/.com/.ca/.co.jp| | Barnes & Noble USA | booksprice. | fetchbook.info | allbookstores | biggerbooks | blackwells | directtextbook | kalahari.net (South Africa) | Special Paperback edition for South Asia.| Download the book too
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
Videolectures
scikit-learn: machine learning in Python — scikit-learn 0.16.1 documentation
Code
Reddit - Dive into anything
Videolectures
pythonic implementation of Bayesian networks for a specific application
Google Code Archive - Long-term storage for Google Code Project Hosting.
https://jmlr.csail.mit.edu/papers/volume10/shah09a/shah09a.pdf
Bayesian networks tutorial
Active learning – Machine Learning (Theory)
Machine learning textbook
rinko2010
