This paper proposes a new multinomial choice model which explicitly takes into account variation in choice sets across observations. The proposed varying choice set logit (VCL) model relaxes the independence of irrelevant alternatives assumption by allowing the individual random utility function to directly depend on choice set types, and can be applied to a variety of data in which some indivi...

This work deals with the unsupervised Bayesian hidden Markov chain restoration extended to the non stationary case. Unsupervised restoration based on “ExpectationMaximization” (EM) or “Stochastic EM” (SEM) estimates considering the “Hidden Markov Chain” (HMC) model is quite efficient when the hidden chain is stationary. However, when the latter is not stationary, the unsupervised restoration re...

Bayesian algorithms have lately been used in a large variety of applications. This paper proposes a new methodology for hyperparameter initialization in the Variational Bayes (VB) algorithm. We employ a dual expectationmaximization (EM) algorithm as the initialization stage in the VB-based learning. In the first stage, the EM algorithm is used on the given data set while the second EM algorithm...

Automatically identifying the research areas of academic/industry researchers is an important task for building expertise organizations or search systems. In general, this task can be viewed as text classification that generates a set of research areas given the expertise of a researcher like documents of publications. However, this task is challenging because the evidence of a research area ma...

Several models aim to measure how demographic, professional, and labour market factors influence unemployment duration, which has a compelling social and economic interest. Nevertheless, this quest still appear challenging in both interpretations and solutions, due to various difficulties. Among others, a relevant issue is the accurate measurement of unemployment, which is often biased by the p...

This paper presents a speaker clustering framework by iteratively performing two stages: a discriminative feature space is obtained given a cluster label set, and the cluster label set is updated using a clustering algorithm given the feature space. In the iterations of two stages, the cluster labels may be different from the true labels, and thus the obtained feature space based on the labels ...

We consider in this article median variants of the learning vector quantization classifier for classification of dissimilarity data. particularly we are interested in optimization of advanced classification quality measures like sensitivity, specificity or the Fβmeasure. These measures are frequently more appropriate than simple accuracy, in particular, if the training data are imbalanced for t...

We consider the problem of clustering high-dimensional data using Gaussian Mixture Models (GMMs) with unknown covariances. In this context, the ExpectationMaximization algorithm (EM), which is typically used to learn GMMs, fails to cluster the data accurately due to the large number of free parameters in the covariance matrices. We address this weakness by assuming that the mixture model consis...

Augmented Conditional Random Fields (ACRFs) are undirected graphical models that maintain the Markov properties of Hidden Markov Models (HMMs), formulated using the maximum entropy (MaxEnt) principle. ACRFs incorporate acoustic context information into an augmented space in order to model the sequential phenomena of the speech signal. The augmented space is constructed using Gaussian activation...

We consider the problem of performing inverse reinforcement learning when the trajectory of the agent being observed is partially occluded from view. Motivated by robotic scenarios in which limited sensor data is available to a learner, we treat the missing information as hidden variables and present an algorithm based on expectationmaximization to solve the non-linear, non-convex problem. Prev...