Preference learning has received increasing attention in both machine learning and information retrieval. The goal of preference learning is to automatically learn a model to rank entities (e.g., documents, webpages, products, music, etc.) according to their degrees of relevance. The particularity of preference learning might be that the training data is a set of pairwise preferences between en...

Due to their flexible nonparametric nature, Gaussian process models are very effective at solving hard machine learning problems. While existing Gaussian process models focus on modeling one single relation, we present a generalized GP model, named multi-relational Gaussian process model, that is able to deal with an arbitrary number of relations in a domain of interest. The proposed model is a...

Processes involving change over time, uncertainty, and rich relational structure are common in the real world, but no general algorithms exist for learning models of them. In this paper we show how Markov logic networks (MLNs), a recently developed approach to combining logic and probability, can be applied to time-changing domains. We then show how existing algorithms for parameter and structu...

The present study tested whether relational memory processes, as measured by the transverse patterning problem, are late-developing in nonhuman primates as they are in humans. Eighteen macaques ranging from 3 to 36 months of age, were trained to solve a set of visual discriminations that formed the transverse patterning problem. Subjects were trained at 3, 4-6, 12, 15-24 or 36 months of age to ...

One main concern towards kernel classifiers is on their sensitivity to the choice of kernel function or kernel matrix which characterizes the similarity between instances. Many realworld data, such as web pages and proteinprotein interaction data, are relational in nature in the sense that different instances are correlated (linked) with each other. The relational information available in such ...

Relational Markov Decision Processes (RMDPs) offer an elegant formalism that combines probabilistic and relational knowledge representations with the decisiontheoretic notions of action and utility. In this paper we motivate RMDPs to address a variety of problems in AI, including open world planning, transfer learning, and relational inference. We describe a symbolic dynamic programming approac...

The present study aims to investigate the system of the visual transitivity by analyzing the images of American English file (2014) and Cutting edge (2005) series based on Halliday’s (1976) systemic functional linguistics and Kress and van leuween’s (1997-2006) social semiotics. The system of visual transitivity refers to a type of process which determines how represented participants are label...

Nonnative English-speaking scholars have often been reported to be at a disadvantage vis-à-vis their English native counterparts when it comes to writing a publishable research article (RA). When they submit their manuscripts to English-language journals, they sometimes receive comments criticizing their faulty English. One area of difficulty for these authors is the grammaticalization of neutr...

KoTOvsKY, LAUHA, and GENTNER, DEDRE. Comparison and Categorization in the Development of Relational Similarity. CHILD DEVELOPMENT, 1996, 67, 2797-2822. 4 experiments investigated the development of children's ability to recognize perceptual relational commonalities such as symmetry or monotonicity. In Experiment 1, 6and 8-year-olds were able to recognize higherorder relational similarity across...