SMALL-SAMPLE EQUATING WITH PRIOR INFORMATION

Small-Sample Equating With Prior Information

Information Diffusion Techniques and Small-Sample Problem

Strong interests in the small-sample problem have been given towards for establishing several information diffusion techniques for pattern recognition. In this paper, we review and formalize three techniques: the soft histogram, the self-study discrete regression, and the interior-outer-set model. To promote the development of this area, in this paper we suggest two open topics: the anti-accura...

Clustering with Prior Information

A fundamental issue in clustering concerns one’s ability (and limitation) to detect clusters, assuming they are built-in to the model that generates the data [1, 4]. Results for the planted partition graph models suggest that clusters can be recovered with arbitrary accuracy if sufficient data (link density) is available [2]. More recently, this problem of cluster detectability has been address...

Learning with prior information

In this paper, a new notion of learnability is introduced, referred to as learnability with prior information (w.p.i.). This notion is weaker than the standard notion of probably approximately correct (PAC) learnability which has been much studied during recent years. A property called “dispersability” is introduced, and it is shown that dispersability plays a key role in the study of learnabil...

Causal Discovery with Prior Information

Bayesian networks (BNs) are rapidly becoming a leading tool in applied Artificial Intelligence (AI). BNs may be built by eliciting expert knowledge or learned via causal discovery programs. A hybrid approach is to incorporate prior information elicited from experts into the causal discovery process. We present several ways of using expert information as prior probabilities in the CaMML causal d...