[Lecture Notes in Computer Science] Subspace, Latent Structure and Feature Selection Volume 3940 || Discrete Component Analysis

[Lecture Notes in Computer Science] Subs

[Lecture Notes in Computer Science] Subspace, Latent Structure and Feature Selection Volume 3940 || Some Aspects of Latent Structure Analysis

✍ Saunders, Craig; Grobelnik, Marko; Gunn, Steve; Shawe-Taylor, John 📂 Article 📅 2006 🏛 Springer Berlin Heidelberg 🌐 English ⚖ 345 KB

This book constitutes the thoroughly refereed post-proceedings of the PASCAL (pattern analysis, statistical modelling and computational learning) Statistical and Optimization Perspectives Workshop on Subspace, Latent Structure and Feature Selection techniques, SLSFS 2005. The 9 revised full papers p

[Lecture Notes in Computer Science] Subs

[Lecture Notes in Computer Science] Subspace, Latent Structure and Feature Selection Volume 3940 || Is Feature Selection Still Necessary?

✍ Saunders, Craig; Grobelnik, Marko; Gunn, Steve; Shawe-Taylor, John 📂 Article 📅 2006 🏛 Springer Berlin Heidelberg 🌐 English ⚖ 525 KB

This book constitutes the thoroughly refereed post-proceedings of the PASCAL (pattern analysis, statistical modelling and computational learning) Statistical and Optimization Perspectives Workshop on Subspace, Latent Structure and Feature Selection techniques, SLSFS 2005. The 9 revised full papers p

[Lecture Notes in Computer Science] Subs

[Lecture Notes in Computer Science] Subspace, Latent Structure and Feature Selection Volume 3940 || Feature Selection for Dimensionality Reduction

✍ Saunders, Craig; Grobelnik, Marko; Gunn, Steve; Shawe-Taylor, John 📂 Article 📅 2006 🏛 Springer Berlin Heidelberg 🌐 English ⚖ 807 KB

This book constitutes the thoroughly refereed post-proceedings of the PASCAL (pattern analysis, statistical modelling and computational learning) Statistical and Optimization Perspectives Workshop on Subspace, Latent Structure and Feature Selection techniques, SLSFS 2005. The 9 revised full papers p

[Lecture Notes in Computer Science] Subs

[Lecture Notes in Computer Science] Subspace, Latent Structure and Feature Selection Volume 3940 || Random Projection, Margins, Kernels, and Feature-Selection

✍ Saunders, Craig; Grobelnik, Marko; Gunn, Steve; Shawe-Taylor, John 📂 Article 📅 2006 🏛 Springer Berlin Heidelberg 🌐 English ⚖ 571 KB

This book constitutes the thoroughly refereed post-proceedings of the PASCAL (pattern analysis, statistical modelling and computational learning) Statistical and Optimization Perspectives Workshop on Subspace, Latent Structure and Feature Selection techniques, SLSFS 2005. The 9 revised full papers p

[Lecture Notes in Computer Science] Subs

[Lecture Notes in Computer Science] Subspace, Latent Structure and Feature Selection Volume 3940 || Less Biased Measurement of Feature Selection Benefits

✍ Saunders, Craig; Grobelnik, Marko; Gunn, Steve; Shawe-Taylor, John 📂 Article 📅 2006 🏛 Springer Berlin Heidelberg 🌐 English ⚖ 431 KB

This book constitutes the thoroughly refereed post-proceedings of the PASCAL (pattern analysis, statistical modelling and computational learning) Statistical and Optimization Perspectives Workshop on Subspace, Latent Structure and Feature Selection techniques, SLSFS 2005. The 9 revised full papers p

[Lecture Notes in Computer Science] Subs

[Lecture Notes in Computer Science] Subspace, Latent Structure and Feature Selection Volume 3940 || Generalization Bounds for Subspace Selection and Hyperbolic PCA

✍ Saunders, Craig; Grobelnik, Marko; Gunn, Steve; Shawe-Taylor, John 📂 Article 📅 2006 🏛 Springer Berlin Heidelberg 🌐 English ⚖ 475 KB

This book constitutes the thoroughly refereed post-proceedings of the PASCAL (pattern analysis, statistical modelling and computational learning) Statistical and Optimization Perspectives Workshop on Subspace, Latent Structure and Feature Selection techniques, SLSFS 2005. The 9 revised full papers p

[Lecture Notes in Computer Science] Subspace, Latent Structure and Feature Selection Volume 3940 || Discrete Component Analysis

✦ Synopsis