badssl.com
badssl.com 🎛Dashboard Dashboard 🎫Certificate expired wrong.host self-signed untrusted-root revoked pinning-test no-common-name no-subject incomplete-chain sha256 sha384 sha512 1000-sans 10000-sans ecc256 ecc384 rsa2048 rsa4096 rsa8192 extended-validation 🎟Client Certificate Certificate Downloads client client-cert-missing 🖼Mixed Content mixed-script very mixed mixed-favicon mixed-form âœï¸HTTP h
グラフラプラシアンã¨ã‹ - Standard ML of Yukkuri
日誌今日ã¯B4ã¯ã˜ã‚ã¦ã®å€‹åˆ¥ã‚¼ãƒŸ(å„自ã®ç ”究ã®é€²æ—ã‚’å ±å‘Šã—ã€å‚åŠ è€…ã‹ã‚‰ã‚¢ãƒ‰ãƒã‚¤ã‚¹ãªã©ã‚’もらã†é›†ã‚Š)ãŒã‚ã‚Šã€çµ‚始ãªã”ã‚„ã‹ãƒ ードã§é€²ã‚€ã€‚自分も発表ã¯æ¥é€±ãªã®ã§ä»–人ã”ã¨ã®ã‚ˆã†ã«ãªã”ã‚“ã§ã„ãŸã€‚ã¨ã‚る発表ã«å…ˆç”ŸãŒã€Œç§ã®Thinkpadã¯ãƒãƒ¥ãƒ¼ãƒªãƒ³ã‚°æ©Ÿæ¢°ã‚ˆã‚Šè³¢ã„。ãªãœã§ã—ょã†ã€ã¨ã„ã†å•ã„ã‹ã‘を出ã—ã€ç§ã‚‚考ãˆæ‚©ã‚“ã ãŒç”ãˆã¯å˜ã«ã€Œæ™®é€šã®ãƒ‘ソコンã¯æ¸©åº¦è¨ˆãªã©ã‚»ãƒ³ã‚µãƒ¼ã«ã‚ˆã‚Šç‰©ç†ç¾è±¡ã‚’観測ã—ã¦ã„ã‚‹ãŒã€ãƒãƒ¥ãƒ¼ãƒªãƒ³ã‚°ã®ãƒ¢ãƒ‡ãƒ«ã¯ãれをå–å¾—ã™ã‚‹è¡“ãŒãªã„ã€ã¨ã„ã†ã ã‘ã®ã“ã¨ã§ã‚ã£ãŸã€‚ドクタã®ã²ã¨ãŒä¸–界全体を入力ã¨ã—ã¦ä¸Žãˆã¦ã„ã‚Œã°ã„ã„ã®ã§ã¯ã€ã¨ã„ã£ãŸåè«–*1ãªã©ã‚‚出ãŸãŒã€ãã‚Œã¯è¨˜è¿°å¯èƒ½ã§ã‚ã‚‹ã‹ã¨ã„ã†è©±ã«ãªã‚Šäº‘々云々。ゼミ後ã¯ç ”究室ã§ã‚°ãƒ©ãƒ•ãƒ™ãƒ¼ã‚¹ã® Semi-Supervised Learning (SSL) ã®è©±é¡Œã¨ã€ãã‚Œã«é–¢é€£ã—ã¦é‡ã¿ä»˜ãグラフã®ãƒŽãƒ¼ãƒ‰åˆ†é¡žã®è©±(特ã«Spectral Clustering)ã«ã¤ã„ã¦èª¿ã¹ã‚‹ã€‚S
COP-KMEANS(Constrained K-means)を試ã—ã¦ã¿ãŸ - ã®ã‚“ã³ã‚Šèªæ›¸æ—¥è¨˜
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Semi-Supervised Learning Tutorial
Semi-Supervised Learning Tutorial Xiaojin Zhu Department of Computer Sciences University of Wisconsin, Madison, USA ICML 2007 Xiaojin Zhu (Univ. Wisconsin, Madison) Semi-Supervised Learning Tutorial ICML 2007 1 / 135 Outline 1 Introduction to Semi-Supervised Learning 2 Semi-Supervised Learning Algorithms Self Training Generative Models S3VMs Graph-Based Algorithms Multiview Algorithms 3 Semi-Super
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http://pages.cs.wisc.edu/~jerryzhu/pub/SSL_EoML.pdf
Tutorial on Semi-Supervised Learning
tech_report_long_8.dvi
Data Clustering via Graph Min-Cuts Erin R. Rhode University of Michigan EECS 767 March 15, 2006 Outline Why Data Clustering? Introduction to Graph Min-Cuts Bipartite Graph Partitioning (Zha et. al., 2001) Minimum-Cut Tree Partitioning (Flake et. al., 200
Graph Cut Algorithms in Vision, Graphics and Machine Learning An Integrative Paper Sudipta N. Sinha { 
Subjectivity Recognition on Word Senses via Semi-supervised Mincuts
http://www.ai.cs.scitec.kobe-u.ac.jp/~t-kosaka/index.php?%E8%B3%87%E6%96%99%E9%83%A8%E5%B1%8B%EF%BC%92
Graph Min-Cuts - Learning from Labeled and Unlabeled Data using Graph Mincuts
http://pages.cs.wisc.edu/~jerryzhu/pub/sslsa.pdf
http://pages.cs.wisc.edu/~goldberg/publications/goldbergTextgraphs.pdf
http://pages.cs.wisc.edu/~goldberg/publications/sslnlp_realisticSSL.pdf
Graph-based SSL - SSL Tutorial: ACL 2008
unlabelled.dvi
Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression
Semi-Supervised Sequential Labeling and Segmentation Using Giga-Word Scale Unlabeled Data
Feature-based Distributed Language Modeling with a Mixture of Experts
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