MOTIVATION In the process of developing risk prediction models, various steps of model building and model selection are involved. If this process is not adequately controlled, overfitting may result in serious overoptimism leading to potentially erroneous conclusions. METHODS For right censored time-to-event data, we estimate the prediction error for assessing the performance of a risk predic...

In Neural Networks (NN), Adaptive Activation Functions (AAF) have parameters that control the shapes of activation functions. These parameters are trained along with other parameters in the NN. AAFs have improved performance of Neural Networks (NN) in multiple classification tasks. In this paper, we propose and apply AAFs on feedforward NNs for regression tasks. We argue that applying AAFs in t...

Single-particle cryo-EM is a powerful approach to determine the structure of large macromolecules and assemblies thereof in many cases at subnanometer resolution. It has become popular to refine or flexibly fit atomic models into density maps derived from cryo-EM experiments. These density maps are typically significantly lower in resolution than electron density maps obtained from X-ray diffra...

Fuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. In this research, FCM is chosen for fuzzy clustering. Parameters such as the number of clusters and the value of fuzzifier significantly influence the extent of generalization of the fuzzy model. These two parameters require tuning to reduce the overfitting in the...

A novel transductive learning algorithm is proposed, which is based on the use of model selection. In its simplest form there are k possible labels, m labeled points and one unlabeled point. One model is built for each possible classification of the unlabeled point yM+1 = Li, i = 1, ..., k, using all m+1 points and m + 1 labels. Any standard model selection criterion can then be applied to sele...

This paper presents two methods for increasing comprehensibility in technical trading rules produced by Genetic Programming. For this application domain adding a complexity penalizing factor to the objective fitness function also avoids overfitting the training data. Using pre-computed derived technical indicators, although it biases the search, can express complexity while retaining comprehens...

We study the characteristics of learning with ensembles. Solving exactly the simple model of an ensemble of linear students, we find surprisingly rich behaviour. For learning in large ensembles, it is advantageous to use under-regularized students, which actually over-fit the training data. Globally optimal performance can be obtained by choosing the training set sizes of the students appropria...

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Sabir Research participated in TREC-2007 in the Million Query and Legal tracks. This writeup focuses on the Legal track, and in particular on the relevance feedback and interactive tasks within the Legal track. The information retrieval software used was the research version of SMART 16.0. SMART was originally developed in the early 1960’s by Gerard Salton and since then has continued to be a l...

This work tests the adaptation of groups from two generalizations of the multiple-player stag hunt to a difficult third version, the notorious weakest-link game. The two training conditions either encouraged or discouraged the development of stable subgroups. Theories of modularization predict that stable subgroups will facilitate coordination in larger groups by helping them “scale up.” Howeve...