Asim Yuzbasioglu An Empirical Analysis of Takeover Predictions in the UK: Application of Artificial Neural Networks and Logistic Regression This study undertakes an empirical analysis of takeover predictions in the UK. The objectives of this research are twofold. First, whether it is possible to predict or identity takeover targets before they receive any takeover bid. Second, to test whether i...

A single student step in an intelligent tutor may involve multiple subskills. Conventional approaches either sidestep this problem, model the step as using only its least known subskill, or treat the subskills as necessary and probabilistically independent. In contrast, we use logistic regression in a Dynamic Bayes Net (LR-DBN) to trace the multiple subskills. We compare these three types of mo...

This paper provides insight into the use of Machine Learning (ML) models for the assessment of humancaused wildfire occurrence. It proposes the use of ML within the context of fire risk prediction, and more specifically, in the evaluation of human-induced wildfires in Spain. In this context, three ML algorithmsdRandom Forest (RF), Boosting Regression Trees (BRT), and Support Vector Machines (SV...

As the electrical industry restructures many of the traditional algorithms for controlling generating units, they need either modification or replacement. In the past, utilities had to produce power to satisfy their customers with the objective to minimize costs and actual demand/reserve were met. But it is not necessary in a restructured system. The main objective of restructured system is to ...

In this paper I will describe the Berkeley (group 1) approach to the GeoCLEF task for CLEF 2005. The main technique we are testing is the fusion of multiple probabilistic searches against different XML components using both Logistic Regression (LR) algorithms and a version of the Okapi BM-25 algorithm. We also combine multiple translations of queries in cross-language searching. Since this is t...

It is shown that the likelihood ratio test statistics are Hodges-Lehmann optimal for testing the null hypothesis against the whole parameter space, provided that certain regularity conditions are fulfilled. These conditions are verified for the non-singular normal, multinomial and Poisson distribution. Let {F 7 ; 7 € S } be a family of probability measures, defined on (X, T) by means of the den...

OBJECTIVE To compare the performance of two predictive radiologic models, logistic regression (LR) and neural network (NN), with five different resampling methods. METHODS One hundred sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross-validation, leave-one-out, a...