Learning valued relations from data

Driven by a large number of potential applications in areas like bioinformatics, information retrieval and social network analysis, the problem setting of inferring relations between pairs of data objects has recently been investigated quite intensively in the machine learning community. To this end, current approaches typically consider datasets containing crisp relations, so that standard classification methods can be adopted. However, relations between objects like similarities and preferences are in many real-world applications often expressed in a graded manner. A general kernel-based framework for learning relations from data is introduced here. It extends existing approaches because both crisp and valued relations are considered, and it unifies existing approaches because different types of valued relations can be modeled, including symmetric and reciprocal relations. This framework establishes in this way important links between recent developments in fuzzy set theory and machine learning. Its usefulness is demonstrated on a case study in document retrieval

The motivation for very early intervention for infants at high risk for autism spectrum disorders

The first Autism Research Matrix (IACC, 2003) listed the identification of behavioural and biological markers of risk for autism as a top priority. This emphasis was based on the hypothesis that intervention with infants at-risk, at an early age when the brain is developing and before core autism symptoms have emerged, could significantly alter the developmental trajectory of children at risk for the disorder and impact long-range outcome. Research has provided support for specific models of early autism intervention (e.g., Early Start Denver Model) for improving outcomes in young children with autism, based on both behavioural and brain activity measures. Although great strides have been made in ability to identify risk markers for autism in younger infant/toddler samples, how and when to intervene during the prodromal state remains a critical question. Emerging evidence suggests that abnormal brain circuitry in autism precedes altered social behaviours; thus, an intervention designed to promote early social engagement and reciprocity potentially could steer brain development back toward the normal trajectory and remit or reduce the expression of symptoms