Multiple Kernel Learning: A Unifying Probabilistic Viewpoint

We present a probabilistic viewpoint to multiple kernel learning unifying well-known regularised risk approaches and recent advances in approximate Bayesian inference relaxations. The framework proposes a general objective function suitable for regression, robust regression and classification that is lower bound of the marginal likelihood and contains many regularised risk approaches as special cases. Furthermore, we derive an efficient and provably convergent optimisation algorithm

Auditory Processing in Children with Specific Language Impairments: Are there Deficits in Frequency Discrimination, Temporal Auditory Processing or General Auditory Processing?

Background/Aims: Specific language impairment (SLI) is believed to be associated with nonverbal auditory (NVA) deficits. It remains unclear, however, whether children with SLI show deficits in auditory time processing, time processing in general, frequency discrimination (FD), or NVA processing in general. Patients and Methods: Twenty-seven children (aged 8-11) with SLI and 27 control children (CG), matched for age and gender, were retrospectively compared with regard to their performance on five NVA skills in terms of just noticeable differences (JND) and time order judgments (TOJ). JND was used for FD, intensity discrimination, and gap detection, while TOJ was used for FD and clicks. Results: Children with SLI performed significantly worse than the CG only on the FD tasks (JND and TOJ). The other nonverbal tasks showed no significant intergroup differences. Additionally, moderate associations were found between the FD tasks and phonological skills, as well as between FD tasks and language scores. Conclusion: Children with SLI appear to have restricted FD skills compared to controls, but there was no evidence for a common NVA deficit or reduced temporal auditory abilities. Copyright (C) 2009 S. Karger AG, Base

Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)

We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs). SKI methods produce kernel approximations for fast computations through kernel interpolation. The SKI framework clarifies how the quality of an inducing point approach depends on the number of inducing (aka interpolation) points, interpolation strategy, and GP covariance kernel. SKI also provides a mechanism to create new scalable kernel methods, through choosing different kernel interpolation strategies. Using SKI, with local cubic kernel interpolation, we introduce KISS-GP, which is 1) more scalable than inducing point alternatives, 2) naturally enables Kronecker and Toeplitz algebra for substantial additional gains in scalability, without requiring any grid data, and 3) can be used for fast and expressive kernel learning. KISS-GP costs O(n) time and storage for GP inference. We evaluate KISS-GP for kernel matrix approximation, kernel learning, and natural sound modelling.Comment: 19 pages, 4 figure

Learning an Interactive Segmentation System

Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. This paper proposes a new evaluation and learning method which brings the user in the loop. It is based on the use of an active robot user - a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem.Comment: 11 pages, 7 figures, 4 table