DDR爱好者之家 Design By 杰米
Name Author Institution Language Exec Multiclass Regression Comments BSVM Chih-Wei Hsu and Chih-Jen Lin National Taiwan University C++ Win Yes Yes   Equbits Foresight Equbits LLC Equbits LLC SDK Win ??? Yes Commercial. Contact Equibits LLC for details Gini-SVM Shantanu Chakrabartty Johns Hopkins University C++ No Yes Yes Handles non positive definite kernels HeroSvm Jianxiong Dong Concordia University C++ Win Yes No Optimized for Pentium 4 LEARNSC Vojislav Kecman   Matlab p-files N/A Yes Yes Must pay for source! LIBSVM Chih-Chung Chang, Chih-Jen Lin National Taiwan University C++, Java, Python, R, MATLAB, Perl, Ruby Win/*nix Yes Yes Graphic interface available LS-SVMlab Kristiaan Pelckmans, Johan Suykens Katholieke Universiteit Leuven Matlab Win/*nix Yes Yes Comes with platform-specific MEX files Matlab SVM Toolbox S. R. Gunn University of Southampton Matlab N/A No Yes Includes a simple GUI mySVM Stefan Ruping Universitat Dortmund C++ Win/*nix No Yes   OSU Junshui Ma, Yi Zhao, and Stanley Ahalt Ohio State University Matlab N/A Yes Yes Matlab interface to LIBSVM Parallel GPDT T. Serafini, G. Zanghirati, L. Zanni Universita di Ferrara C++ No No No Designed for parallel systems pcSVM
Procoders.net C - No No   RVMs Mike Tipping MSR Cambridge Matlab - Yes Yes   SpiderSVM Jason Weston, Andre Elisseeff , Gokhan BakIr , Fabian Sinz Max Planck Institute for Biological Cybernetics Matlab N/A Yes Yes Part of the Spider machine learning library Statistical Pattern Recognition Toolbox for MATLAB Vojtech Franc and Vaclav Hlavac Czech Technical University Prague Matlab/C No Yes No Good online documentation. Everything I′ve tried has worked 字串9
well. Lots of stuff besides SVMs. SVMdark Martin Sewell University College London C Win No Yes   SvmFu Ryan Rifkin MIT C++ No   No Must be compiled with g++ SVMLight Thorsten Joachims Cornell University C Win/*nix No Yes   SVMsequel Hal Daume III University of Southern California OCaml No Yes No "Very fast and handles enormous datasets nicely" SVMtorch Ronan Collobert and Samy Bengio IDIAP C++ No ? Yes   SVM Toolbox Gavin Cawley University of East Anglia Norwich Matlab/C++ No Yes No Beta version WinSVM Martin Sewell University College London C++ Win No Yes  
DDR爱好者之家 Design By 杰米
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DDR爱好者之家 Design By 杰米

《魔兽世界》大逃杀!60人新游玩模式《强袭风暴》3月21日上线

暴雪近日发布了《魔兽世界》10.2.6 更新内容,新游玩模式《强袭风暴》即将于3月21 日在亚服上线,届时玩家将前往阿拉希高地展开一场 60 人大逃杀对战。

艾泽拉斯的冒险者已经征服了艾泽拉斯的大地及遥远的彼岸。他们在对抗世界上最致命的敌人时展现出过人的手腕,并且成功阻止终结宇宙等级的威胁。当他们在为即将于《魔兽世界》资料片《地心之战》中来袭的萨拉塔斯势力做战斗准备时,他们还需要在熟悉的阿拉希高地面对一个全新的敌人──那就是彼此。在《巨龙崛起》10.2.6 更新的《强袭风暴》中,玩家将会进入一个全新的海盗主题大逃杀式限时活动,其中包含极高的风险和史诗级的奖励。

《强袭风暴》不是普通的战场,作为一个独立于主游戏之外的活动,玩家可以用大逃杀的风格来体验《魔兽世界》,不分职业、不分装备(除了你在赛局中捡到的),光是技巧和战略的强弱之分就能决定出谁才是能坚持到最后的赢家。本次活动将会开放单人和双人模式,玩家在加入海盗主题的预赛大厅区域前,可以从强袭风暴角色画面新增好友。游玩游戏将可以累计名望轨迹,《巨龙崛起》和《魔兽世界:巫妖王之怒 经典版》的玩家都可以获得奖励。