[ACM Press Twenty-first international conference - Banff, Alberta, Canada (2004.07.04-2004.07.08)] Twenty-first international conference on Machine learning - ICML '04 - Learning to learn with the informative vector machine

[ACM Press Twenty-first international co

[ACM Press Twenty-first international conference - Banff, Alberta, Canada (2004.07.04-2004.07.08)] Twenty-first international conference on Machine learning - ICML '04 - Diverse ensembles for active learning

✍ Melville, Prem; Mooney, Raymond J. 📂 Article 📅 2004 🏛 ACM Press ⚖ 222 KB

[ACM Press Twenty-first international co

[ACM Press Twenty-first international conference - Banff, Alberta, Canada (2004.07.04-2004.07.08)] Twenty-first international conference on Machine learning - ICML '04 - Gradient LASSO for feature selection

✍ Kim, Yongdai; Kim, Jinseog 📂 Article 📅 2004 🏛 ACM Press ⚖ 292 KB

[ACM Press Twenty-first international co

[ACM Press Twenty-first international conference - Banff, Alberta, Canada (2004.07.04-2004.07.08)] Twenty-first international conference on Machine learning - ICML '04 - Learning a kernel matrix for nonlinear dimensionality reduction

✍ Weinberger, Kilian Q.; Sha, Fei; Saul, Lawrence K. 📂 Article 📅 2004 🏛 ACM Press ⚖ 1022 KB

[ACM Press Twenty-first international co

[ACM Press Twenty-first international conference - Banff, Alberta, Canada (2004.07.04-2004.07.08)] Twenty-first international conference on Machine learning - ICML '04 - Multiple kernel learning, conic duality, and the SMO algorithm

✍ Bach, Francis R.; Lanckriet, Gert R. G.; Jordan, Michael I. 📂 Article 📅 2004 🏛 ACM Press ⚖ 191 KB

[ACM Press Twenty-first international co

[ACM Press Twenty-first international conference - Banff, Alberta, Canada (2004.07.04-2004.07.08)] Twenty-first international conference on Machine learning - ICML '04 - Multiple kernel learning, conic duality, and the SMO algorithm

✍ Bach, Francis R.; Lanckriet, Gert R. G.; Jordan, Michael I. 📂 Article 📅 2004 🏛 ACM Press ⚖ 191 KB

While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the optimization of the coefficient

[ACM Press Twenty-first international co

[ACM Press Twenty-first international conference - Banff, Alberta, Canada (2004.07.04-2004.07.08)] Twenty-first international conference on Machine learning - ICML '04 - K -means clustering via principal component analysis

✍ Ding, Chris; He, Xiaofeng 📂 Article 📅 2004 🏛 ACM Press ⚖ 247 KB