By always mapping data from lower dimensional space into higher or even infinite dimensional space, kernel k-means is able to organize data into groups when data of different clusters are not linearly separable. However, kernel k-means incurs the large scale computation due to the representation theorem, i.e. keeping an extremely large kernel matrix in memory when using popular Gaussian and spa...

"> In the present study the reaction kinetic and dynamic modeling of the reactor for syngas transformation into dimethyl ether u...

We consider a rich tanker trailer routing problem with stochastic transit times for chemicals and liquid bulk orders. A typical route of the tanker trailer comprises of sourcing a cleaned and prepped trailer from a pre-wash location, pickup and delivery of chemical orders, cleaning the tanker trailer at a post-wash location after order delivery and prepping for the next order. Unlike traditiona...

In this paper, we introduce a kernel for monotone data derived from the Choquet integral with its underlying fuzzy measure. While a naïve computation of this kernel has a complexity that is exponential in the number of data attributes, we propose a more efficient approach with quadratic time complexity. Kernel PCA and SVM classification are employed to illustrate characteristics and benefits of...

Representation learning is an important step in the machine pipeline. Given current biological sequencing data volume, explicit representation prohibitive due to dimensionality of resulting feature vectors. Kernel-based methods, e.g., SVM, are a proven efficient and useful alternative for several (ML) tasks such as sequence classification. Three challenges with kernel methods (i) computation ti...

In this paper we introduce a framework for learning from RDF data using graph kernels that count substructures in RDF graphs, which systematically covers most of the existing kernels previously defined and provides a number of new variants. Our definitions include fast kernel variants that are computed directly on the RDF graph. To improve the performance of these kernels we detail two strategi...

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In recent years, size of VLSI circuits is dramatically grown and layout generation of current circuits has become a dominant task in design flow. Standard cell placement is an effective stage of physical design and quality of placement affects directly on the performance, power consumption and signal immunity of design. Placement can be performed analytically or heuristically. Analytical placer...

We study a decomposition-based scalable approach to performing kernel ridge regression. The method is simple to describe: it randomly partitions a dataset of size N into m subsets of equal size, computes an independent kernel ridge regression estimator for each subset, then averages the local solutions into a global predictor. This partitioning leads to a substantial reduction in computation ti...