Balanced Partitioning and Hierarchical Clustering at ScalePhilosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more
Model evaluation, model selection, and algorithm selection in machine learning
A single-PDF version of Model Evaluation parts 1-4 is available on arXiv: https://arxiv.org/abs/1811.12808 Introduction Machine learning has become a central part of our life – as consumers, customers, and hopefully as researchers and practitioners! Whether we are applying predictive modeling techniques to our research or business problems, I believe we have one thing in common: We want to make “g
Back-to-Basics Weekend Reading - Survey of Local Algorithms
Back-to-Basics Weekend Reading - Survey of Local AlgorithmsMay 29, 2015 • 132 words As we know the run time of most algorithms increases when the input set increases in size. There is one noticeable exception: there is a class of distributed algorithms, dubbed local algorithms, that run in constant time, independently of the size of the network. Being highly scalable and fault tolerant, such algor
Design patterns for efficient graph algorithms in MapReduce
Design Patterns for Efficient Graph Algorithms in MapReduce Jimmy Lin and Michael Schatz University of Maryland, College Park {jimmylin,mschatz}@umd.edu ABSTRACT Graphs are analyzed in many important contexts, includ- ing ranking search results based on the hyperlink struc- ture of the world wide web, module detection of protein- protein interaction networks, and privacy analysis of social network
HyperLogLog++: Google’s Take On Engineering HLL –
Matt Abrams recently pointed me to Google’s excellent paper “HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm” [UPDATE: changed the link to the paper version without typos] and I thought I’d share my take on it and explain a few points that I had trouble getting through the first time. The paper offers a few interesting improvements that are w
データマイニングで使われるトップ10アルゴリズム - データサイエンティスト上がりのDX参謀・起業家
2006年のデータマイニング学会、IEEE ICDMで選ばれた「データマイニングで使われるトップ10アルゴリズム」に沿って機械学習の手法を紹介します（この論文は@doryokujin君のポストで知りました、ありがとうございます！）。 必ずしも論文の内容には沿っておらず個人的な私見も入っていますので、詳細は原論文をご確認下さい。また、データマイニングの全体観をサーベイしたスライド資料がありますので、こちらも併せてご覧下さい。 データマイニングの基礎 View more presentations from Issei Kurahashi 1. C4.5 C4.5はCLSやID3といったアルゴリズムを改良してできたもので、決定木を使って分類器を作ります。決定木といえばCARTが良く使われますが、CARTとの違いは以下のとおりです。 CARTは2分岐しかできないがC4.5は3分岐以上もできる C
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
http://www.vldb.org/pvldb/vol11/p2102-faloutsos.pdf
Bandit Problems
Understanding Dropout
Unsupervised Computer Vision: The State of the Art | Stitch Fix Technology – Multithreaded
http://data-people.cs.illinois.edu/courses/cs598/papers/turkit.pdf
A taxonomy of sequential pattern mining algorithms
https://dl.acm.org/citation.cfm?doid=2452376.2452456
http://outlier-analytics.org/odd13kdd/papers/guzman,poblete.pdf
Dimension Independent Matrix Square using MapReduce
https://tech.nextroll.com/media/hllminhash.pdf
http://research.google.com/pubs/archive/40671.pdf
https://algo.inria.fr/flajolet/Publications/DuFl03.pdf
HyperLogLog: the analysis of a near-optimal cardinality estimation algorithm (PDF形式)
http://homepage.informatik.fh-gelsenkirchen.de/conen/vorlesungen/ss04/gin2-ss04/fast-implementations-of-shortest-path.pdf