This paper presents our approach to the PAN 2012 Traditional Authorship Attribution tasks and the Sexual Predator Identification task. We approached these tasks with machine learning methods that work at the character level. More precisely, we treated texts as just sequences of symbols (strings) and used string kernels in conjunction with different kernel-based learning methods: supervised and ...

The evidence for character displacement as a widespread response to competition is now building. This progress is largely the result of the establishment of rigorous criteria for demonstrating character displacement in the animal literature. There are, however, relatively few well-supported examples of character displacement in plants. This review explores the potential for character displaceme...

The rst processing step in computational early vision usually consists of convolutions with a number of kernels. These kernels often are derived from a mother kernel that is rotated, scaled, or deformed with respect to other degrees of freedom. This paper presents an eecient computational approach to calculate the responses of arbitrary mother kernels with arbitrary deformations. Analytical sol...

Data Parallelism and Kernels Data parallelism is a form of parallelism across multiple processors that is achieved when each processor performs identical tasks on different pieces of distributed data. Data-parallel portions of an algorithm are executed on devices as kernels, which are C functions with some restrictions and a few language extensions. The host launches kernels across a 1D, 2D, or...

Dense linear algebra kernels such as matrix multiplication have been used as benchmarks to evaluate the effectiveness of many automated compiler optimizations. However, few studies have looked at collectively applying the transformations and parameterizing them for external search. In this paper, we take a detailed look at the optimization space of three dense linear algebra kernels. We use a t...

We present how to perform exact large-scale multi-class Gaussian process classification with parameterized histogram intersection kernels. In contrast to previous approaches, we use a full Bayesian model without any sparse approximation techniques, which allows for learning in sub-quadratic and classification in constant time. To handle the additional model flexibility induced by parameterized ...

In this paper, the effect of infrared (IR) roasting conditions on Aflatoxins and total counts of two types of Iranian pistachio kernels, long and round, was studied. The optimized roasting conditions, 70 V for round pistachios and 90 V for long pistachios with 10 cm distance from IR source were used. Naturally Aflatoxin-contaminated kernels were supplied and roasted. Microbiological an...

Recent applications and developments based on support vector machines (SVMs) have shown that using multiple kernels instead of a single one can enhance classifier performance. However, there are few reports on performance of the kernel‐based Fisher discriminant analysis (kernel‐based FDA) method with multiple kernels. This paper proposes a multiple kernel construction ...