We developed a new speaker verification system that is robust to intra-speaker variation. There is a strong likelihood that intraspeaker variations will occur due to changes in talking styles, the periods when an individual speaks, and so on. It is well known that such variation generally degrades the performance of speaker verification systems. To solve this problem, we applied multiple kernel...

We consider a Multiple Kernel Learning (MKL) framework for nuclei classification in tissue microarray images of renal cell carcinoma. Several features are extracted from the automatically segmented nuclei and MKL is applied for classification. We compare our results with an incremental version of MKL, support vector machines with single kernel (SVM) and voting. We demonstrate that MKL inherentl...

In this paper, we point out that there exist scaling and initialization problems in most existing multiple kernel learning (MKL) approaches, which employ the large margin principle to jointly learn both a kernel and an SVM classifier. The reason is that the margin itself can not well describe how good a kernel is due to the negligence of the scaling. We use the ratio between the margin and the ...

The goal of Multiple Kernel Learning (MKL) is to combine kernels derived from multiple sources in a data-driven way with the aim to enhance the accuracy of a target kernel machine. State-of-the-art methods of MKL have the drawback that the time required to solve the associated optimization problem grows (typically more than linearly) with the number of kernels to combine. Moreover, it has been ...

Recently, the field of neuroimaging analysis has seen a large number of studies which use machine learning methods to make predictions about the progression of Alzheimer’s Disease (AD) in mildly demented subjects. Among these, Multi-Kernel Learning (MKL) has emerged as a powerful tool for systematically aggregating diverse data views, and several groups have shown that MKL is uniquely suited to...

We present generalization bounds for the TS-MKL framework for two stage multiple kernel learning. We also present bounds for sparse kernel learning formulations within the TS-MKL framework.

Spatial pyramid matching has recently become a promising technique for image classification. Despite its success and popularity, no prior work has tackled the problem of learning the optimal spatial pyramid representation for the given image data and the associated object category. We propose a Multiple Scale Learning (MSL) framework to learn the best weights for each scale in the pyramid. Our ...

We present a geometric formulation of the Multiple Kernel Learning (MKL) problem. To do so, we reinterpret the problem of learning kernel weights as searching for a kernel that maximizes the minimum (kernel) distance between two convex polytopes. This interpretation combined with novel structural insights from our geometric formulation allows us to reduce the MKL problem to a simple optimizatio...

In math education the goal is for children not only to master the materials and problems presented, but to understand underlying principles and properties that can be applied broadly to new problems and situations. Teachers in the classroom and policy-makers in Washington thus are both faced with what is essentially a cognitive question: What instructional regimes and practices will produce rap...

We establish the equivalence of conjectures concerning the pair correlation of zeros of L-functions in the Selberg class and the variances of sums of a related class of arithmetic functions over primes in short intervals. This extends the results of Goldston and Montgomery [‘Pair correlation of zeros and primes in short intervals’, Analytic number theory and Diophantine problems (Stillwater, 19...