Kernel Logistic Regression (KLR) is a powerful classification technique that has been applied successfully in many classification problems. However, it is often not found in large-scale data classification problems and this is mainly because it is computationally expensive. In this paper, we present a new KLR algorithm based on Truncated Regularized Iteratively Reweighted Least Squares(TR-IRLS)...

A large and geographically diverse data set consisting of meandering, braiding, incising, and post-incision equilibrium streams was used in conjunction with logistic regression analysis to develop a probabilistic approach to predicting thresholds of channel pattern and instability. An energy-based index was developed for estimating the risk of channel instability associated with specific stream...

The softmax link is used in many probabilistic model dealing with both discrete and continuous data. However, efficient Bayesian inference for this type of model is still an open problem due to the lack of efficient upper bound for the sum of exponentials. We propose three different bounds for this function and study their approximation properties. We give a direct application to the Bayesian t...

The objective of this study was to develop a probabilistic model to predict the end of lag time (λ) during the growth of Bacillus cereus vegetative cells as a function of temperature, pH, and salt concentration using logistic regression. The developed λ model was subsequently combined with a logistic differential equation to simulate bacterial numbers over time. To develop a novel model for λ, ...

A data stream is an ordered sequence of training instances arriving at a rate that does not permit to permanently store them in memory and leads to the necessity of online learning methods when trying to predict some hidden target variable. In addition, concept drift often occurs, what means means that the statistical properties of the target variable may change over time. In this paper, we pre...

in this paper, a new enhanced version of the particle swarm optimization (pso) is presented. an important modification is made by adding probabilistic functions into pso, and it is named probabilistic particle swarm optimization (ppso). since the variation of the velocity of particles in pso constitutes its search engine, it should provide two phases of optimization process which are: explorati...

We study capacitated assortment problems when customers choose under the multinomial logit model with nested consideration sets. In this choice model, there are multiple customer types and a customer of a particular type is interested in purchasing only a particular subset of products. We use the term consideration set to refer to the subset of products that a customer of a particular type is i...