Jain and colleagues pushed boundaries in their study by focusing on colonoscopy procedure time and the factors that may influence this. Two of these factors included the level of experience of the endoscopist and the time of day with procedures performed in the afternoon associated with poorer outcomes. As a medical educationalist and gastroenterologist, I want to share scholarship with the aca...

This paper presents a new procedure for learning mixtures of independent component analyzers. The procedure includes non-parametric estimation of the source densities, supervised-unsupervised learning of the model parameters, incorporation of any independent component analysis (ICA) algorithm into the learning of the ICA mixtures, and estimation of residual dependencies after training for corre...

Collective classification refers to the classification of interlinked and relational objects described as nodes in a graph. The Iterative Classification Algorithm (ICA) is a simple, efficient and widely used method to solve this problem. It is representative of a family of methods for which inference proceeds as an iterative process: at each step, nodes of the graph are classified according to ...

Efficiency of the first-order logic proof procedure is a major issue when deduction systems are to be used in real environments, both on their own and as a component of larger systems (e.g., learning systems). Hence, the need of techniques that can speed up such a process. This paper proposes a new algorithm for matching first-order logic descriptions under θ-subsumption that is able to return ...

Efforts have been directed at obtaining flexible learning procedures that optimally adapt to various possible characteristics of the data generating mechanism. A question that addresses the issue of how far one can go in this direction is: Given a regression procedure, however sophisticated it is, how many regression functions are estimated accurately? In this work, for a given sequence of pres...

Selection procedure • The initial state form n inputs only, there are no neurons. • If there are k neurons already, the probability of a selection from inputs and from neurons is given by constant probability p0 that one of the network inputs will be selected. • Otherwise an already existing neuron is selected randomly with probability proportional to fitness. • After the new neuron is formed a...

Learning tasks from a single demonstration presents a significant challenge because the observed sequence is inherently an incomplete representation of the procedure that is specific to the current situation. Observation-based machine-learning techniques are not effective without multiple examples. However, when a demonstration is accompanied by natural language explanation, the language provid...

A wrapper variable selection procedure is proposed for use with learning machines that generate a measure of variable importance, such as Random Forest. The procedure is based on iteratively removing low-ranking variables and assessing the learning machine performance by cross-validation. The procedure is implemented for Random Forest on some QSAR modeling examples from drug discovery and devel...