PASCAL Network

Number of records: 5.

	In recent years, learning methods are desirable because of their reliability and efficiency in real-world problems. We propose a novel method to find infinitely many kernel combinations for learning problems with the help of infinite and semi-infinite optimization regarding all elements in kernel space. This will provide to study variations of combinations of kernels when considering heterogeneous data in real-world applications. Looking at all infinitesimally fine convex combinations of the kernels from the infinite kernel set, the margin is maximized subject to an infinite number of constraints with a compact index set and an additional (Riemann-Stieltjes) integral constraint due to the combinations. After a parametrisation in the space of probability measures it becomes semi-infinite. We analyze the conditions which satisfy the Reduction Ansatz and discuss the type of distribution functions of the kernel coefficients within the structure of the constraints and our bilevel optimization problem. Sureyya Ozogur and G.W. Weber Conference or Workshop Item (2008)
	Mathematical Modelling of Enzymatic Reactions: Simulation and Parameter Estimation by Genetic Algorithm Sureyya Ozogur Book Item not available online. (26 February 2009)
	MODELLING OF KERNEL MACHINES BY INFINITE AND SEMI-INFINITE PROGRAMMING Sureyya Ozogur and G.W. Weber Conference or Workshop Item (2008)
	Pattern analysis for the prediction of fungal pro-peptide cleavage sites Sureyya Ozogur, John Shawe-Taylor, G.W. Weber and Z.B. Ögel Article (11 June 2008)
	Prediction with the SVM using test point margins Sureyya Ozogur, Zakria Hussain and John Shawe-Taylor Book Section (31 January 2008)

This list was generated on Wed May 22 22:34:25 BST 2013.