... approach. However, user preferences are undisclosed and different from user to user. The current developments in machine learning ... process can adapt to the user. Current hardware capabilities allow to process a large amount of data ...

Provides a comprehensive review of kernel mean embeddings of distributions and, in the course of doing so, discusses some challenging issues that could potentially lead to new research directions.

... Gaussian Process Latent Variable Model (GPLVM) [51– 54] was studied. It is a non-parametric technique that can ... metric can be computed by using either pairwise loss or triplet loss. For the pairwise loss, its related methods ...

... Gaussian process approach for preference learning , by Chu and Ghahramani [ 8 ] . Support Vector Classifier ( SVC ) . To apply SVC to preference learning , we first ... Approach to Preference Learning with Interaction Terms 839.

Despite these benefits, GPs are typically not applied to datasets with more than a few thousand data points. This is in part due to an inference procedure that requires matrix inverses, determinants, and other expensive operations.

... Gaussian process to include the preferences imposed by the ML and CL constraints. Recently, Pei et al. [9] propose a discriminative clustering model that uses relative comparisons and, like our method, can also make use of unspecified ...

... Gaussian process models provide a probabilistic non-para- metric modelling approach for black-box identification of nonlinear dy- namic systems. The Gaussian processes can highlight areas of the in- put space where prediction quality is ...

... metric learning method for human pose estimation , namely MTIK [ 3 ] , the Relevance Vector Machine ( RVM ) [ 2 ] , the Twing Gaussian Process ... approach to human pose estimation , in all activities with different number of training ...

... learning .... lp-norm multiple kernel learning algorithm . . . . . . . . . . . . . . . . . Heterogeneousfeatureaugmentation......................... Information-theoretic metric ... Gaussian process regression algorithm ...

... metric learning (Bar-Hillel et al., 2003), and graphical modeling (Getoor et al., 2002). The reciprocal relations ... Gaussian processes (GP) (Rasmussen & Williams, 2006), which leads to a data-dependent covari- ance/kernel ...