Among the suitable machine learning algorithms, we apply the oligo kernel and the radial basis function kernel to the coreceptor usage prediction problem of HIV by employing the framework to calculate the kernel
However, it does not allow for discontinuity which typically arises in real-world reinforcement learning tasks. In this paper, we propose a new basis function based on geodesic Gaussian kernels , which exploits the non-linear manifold structure induced by the Markov decision processes. The ...
It is one of the most preferred and used kernel functions in svm. It is usually chosen for non-linear data. It helps to make proper separation when there is no prior knowledge of data. Gaussian Radial Basis Formula F(x, xj) = exp(-gamma * ||x - xj||^2) The value of gamma vari...
As we outline below, creating an exact GP that learns and utilizes naturally-occurring sparsity shall require three main building blocks: (1) ultra-flexible, non-stationary, compactly-supported kernel functions, specially customized to learn and encode zero-covariances, (2) a high-performing implemen...
(2009) proposed algorithms for approximately solv- ing (2) while preserving differential privacy. However, their algorithms are either restricted to linear decision functions (f (w, x) = w, x ) (which has polynomial dependence of the error on the dimensionality of the feature space) or non-...
There- fore, the existing methods do not allow implementing SVM in data that cannot be classified by linear deci- sion functions. The best approaches to work with non-linear kernels are wrapper methods because filter methods are less efficient than wrapper methods and embedded methods are focused...
In this paper, we propose a new form of regularization that is able to utilize the label information of a data set for learning kernels. The proposed regularization, referred to as ideal regularization, is a linear function of the kernel matrix to be learned. The ideal regularization allows us...
An interesting special H H n ´ to our algorithm is that Csato and Opper repeatedly project on case is X = R with k(x, y) = x, y (the normal dot-product to a low-dimensional subspace, which can be computationally in Rn) which corresponds to learning linear functions in Rn , ...
The spectral kernel machine combines kernel functions and spectral graph theory for solving problems of machine learning. The data points in the dataset are placed in the form of a matrix known as a kernel matrix, or Gram matrix, containing all pairwise kernels between the data points. The dat...
Table 4: Summary of the feedforward and recurrent neural architectures and the corresponding hyperparameters used in the experiments. GP-based models used the same architectures as their non-GP counterparts. Activations are given for the hidden units; vanilla neural nets used linear output activations...