In minimally invasive laparoscopic surgery, it is of practical significance to quickly locate the location and category information surgical instrument. It can remind medical personnel irreversible injury caused patients due leaving instruments after operation. this paper, Gaussian kernel introduced into each ground truth, which conducive making full use label allocate positive negative samples...

The object of Bayesian modelling is the predictive distribution, which in a forecasting scenario enables evaluation of forecasted values and their uncertainties. In this paper we focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models such as the Gaussian Process and the Relevance Vector Machine. We derive novel analytic expressions ...

Background: Alzheimer's disease (AD) is the most common disorder of dementia, which has not been cured after its occurrence. AD progresses indiscernible, first destroy the structure of the brain and subsequently becomes clinically evident. Therefore, the timely and correct diagnosis of these structural changes in the brain is very important and it can prevent the disease or stop its progress. N...

Kernel methods have been widely used in data classification. Many kernel-based classifiers like Kernel Support Vector Machines (KSVM) assume that data can be separated by a hyperplane in the feature space. These methods do not consider the data distribution. This paper proposes a novel Kernel Maximum A Posteriori (KMAP) classification method, which implements a Gaussian density distribution ass...

The equivalent kernel [1] is a way of understanding how Gaussian process regression works for large sample sizes based on a continuum limit. In this paper we show (1) how to approximate the equivalent kernel of the widely-used squared exponential (or Gaussian) kernel and related kernels, and (2) how analysis using the equivalent kernel helps to understand the learning curves for Gaussian proces...

This paper introduces and evaluates a novel approach for unsupervised speaker change detection. In many unsupervised speaker change detection algorithms, each audio segment is typically modeled with a multivariate single Gaussian density, where it is assumed that the distribution of the speech features of the segment is Gaussian. However, this assumption is too strong in many cases. Therefore, ...

Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. Gaussian Process regression is a popular technique for modeling the input-output relations of a set of variables under the assumption that the weight vector has a Gaussian prior. However, it is challenging to apply Gaussian Process regression to lar...

To assist later deriviations and avoid reiterating the material in the main paper, we briefly present important equations for Sparse Spectrum GPs (SSGPs). Based on Bochner’s theorem, continuous shift-invariant kernels can be unbiasedly approximated by an explicit finitedimensional feature map. Leveraging this approximation, we consider SSGPs which is a class of Gaussian processes with kernel in...

In this work, we draw attention to a connection between skill-based models of game outcomes and Gaussian process classification models. The Gaussian process perspective enables a) a principled way of dealing with uncertainty and b) rich models, specified through kernel functions. Using this connection, we tackle the problem of predicting outcomes of football matches between national teams. We d...