We provide guarantees for approximate Gaussian Process (GP) regression resulting from two common low-rank kernel approximations: based on random Fourier features, and truncating the kernel's Mercer expansion. In particular, we bound Kullback–Leibler divergence between an exact GP one of afore-described approximations to its kernel, as well their corresponding predictive densities, also error me...

Herein, I present some notes concerning implementation of now classical method of data clustering, called Maximum Mutual Information Clustering. It was introduced in [Mercer et al., 1992] in context of language modeling. The original article contained some cues concerning its implementation. These are carried out in detail here, together with some new tricks. Results of the test run on 110M wor...

One of the challenging problems in forecasting the conditional volatility of stock market returns is that general kernel functions in support vector machine (SVM) cannot capture the cluster feature of volatility accurately. While wavelet function yields features that describe of the volatility time series both at various locations and at varying time granularities, so this paper construct a mul...

Concern continues to be expressed about the rapid rise of health care costs in the United States, its present impact, implications for the future, and alternative policies aimed at moderating the increase. For example, a 1990 survey of 821 chief executive officers of businesses in Wisconsin revealed that 67 percent perceived health costs as "substantially out of control" with 96 percent respond...

West Nile virus (WNV) was detected by Taqman reverse transcription-polymerase chain reaction in 4 of 85 (4.7%) blood-engorged (n = 2) and unengorged (n = 2) Icosta americana (Leach) hippoboscid flies that were collected from wild raptors submitted to a wildlife rehabilitation center in Mercer County, NJ, in 2003. This report represents an additional detection of WNV in a nonculicine arthropod i...

AbsIracf-In this paper, the generalization ability 01 empirical risk minimization algorithms is investigated in the cootext of dislribution-lm prohably appmxjmalely comecl (PAC) learning. We identily a class of empirical risk minimization algorithms that are PAC, and show that the generic version of the support vector regression method belongs lo the class lor any given Mercer kernel. Moreover,...