This paper presents an image reconstruction method for positron-emission tomography (PET) based on a penalized, weighted least-squares (PWLS) objective. For PET measurements that are precorrected for accidental coincidences, we argue statistically that a least-squares objective function is as appropriate, if not more so, than the popular Poisson likelihood objective. We propose a simple data-ba...

Confidence measure plays an important role in keyword spotting. To enhance the effectiveness of the confidence measure, we propose a novel method which improves the performance of keyword spotting by directly maximizing the area under the ROC curve (AUC). Firstly, we approximate the AUC as an objective function with the weighted mean confidence measure. Then, we optimize the objective function ...

We investigate the use of voicing in state-of-the-art Large Vocabulary Continuous Audio-visual automatic Speech Recognition (AV-LVCSR). In this work we apply an original adaptive weighting function using voicing level to estimate the appropriate combination weights for each of the modalities. We show that we can improve the state-of-the-art AV-LVCSR performance under speech noise by using a det...

In this paper, we consider online prediction from expert advice in a situation where each expert observes its own loss at each time while the loss cannot be disclosed to others for reasons of privacy or confidentiality preservation. Our secure exponential weighting scheme enables exploitation of such private loss values by making use of cryptographic tools. We proved that the regret bound of th...

Minimization of a stochastic cost function is commonly used for approximate sampling in high-dimensional Bayesian inverse problems with Gaussian prior distributions and multimodal posterior distributions. The density the samples generated by minimization not desired target density, unless observation operator linear, but distribution useful as proposal importance or Markov chain Monte Carlo met...

A uni ed framework for 3{D shape reconstruction allows us to combine image-based and geometry-based information sources. The image information is akin to stereo and shape-fromshading, while the geometric information may be provided in the form of 3{D points, 3{D features or 2{D silhouettes. A formal integration framework is critical in recovering complicated surfaces because the information fro...

Feature weighting is known empirically to improve classification accuracy for k-nearest neighbor classifiers in tasks with irrelevant features. Many feature weighting algorithms are designed to work with symbolic features, or numeric features, or both, but cannot be applied to problems with features that do not fit these categories. This paper presents a new k-nearest neighbor feature weighting...

Text categorization is a task of automatically assigning documents to a set of predefined categories. Usually it involves a document representation method and term weighting scheme. This paper proposes a new term weighting scheme called Modified Inverse Document Frequency (MIDF) to improve the performance of text categorization. The document represented in MIDF is trained using the support vect...

Despite extensive studies, the nature of risk attitudes remains a vigorously discussedquestion in economics and psychology. In expected utility theory, attitudes towardsrisk originate from changes in marginal utility. Cumulative prospect theory (CPT)adds an additional dimension: the weighting of probabilities. By examining bothdimensions, we strive to gain more insight on the re...

The assessment of models of financial market behaviour requires evaluation tools. When complexity hinders a direct estimation approach, e.g., for agent based microsimulation models or complex multifractal models, simulation based estimators might provide an alternative. In order to apply such techniques, an objective function is required, which should be based on robust statistics of the time s...