Multilateration (MLAT) is the de facto technique to localize points of interest (POIs) in navigation and surveillance systems. Despite sensors being inherently noisy, most existing techniques i) are oblivious to noise patterns in sensor measurements; and ii) only provide point estimates of the POI. This often results in unreliable estimates with high variance, i.e., that are highly sensitive to measurement noise. To overcome this caveat, we advocate the use of Bayesian modeling. Using Bayesian statistics, we provide a comprehensive guide to handle uncertainties in MLAT, including principled choices for the likelihood function and the prior distributions. Notably, the resulting model is easy to implement and can leverage off-the-shelf Markov Chain Monte Carlo (MCMC) software for inference. Besides coping with unreliable measurements, our framework can also deal with sensors whose location is not completely known, which is an asset in mobile systems. Our solution also naturally incorporates multiple measurements per reference point, a common practical situation that is usually not handled directly by other approaches. Comprehensive experiments with both synthetic and real-world data indicate that our Bayesian approach to the MLAT task provides better position estimation and uncertainty quantification when compared to the available alternatives.

The Minimal Learning Machine (MLM) is a nonlinear supervised approach based on learning a linear mapping between distance matrices computed in the input and output data spaces, where distances are calculated using a subset of points called reference points. Its simple formulation has attracted several recent works on extensions and applications. In this paper, we aim to address some open questions related to the MLM. First, we detail theoretical aspects that assure the interpolation and universal approximation capabilities of the MLM, which were previously only empirically verified. Second, we identify the task of selecting reference points as having major importance for the MLM's generalization capability. Several clustering-based methods for reference point selection in regression scenarios are then proposed and analyzed. Based on an extensive empirical evaluation, we conclude that the evaluated methods are both scalable and useful. Specifically, for a small number of reference points, the clustering-based methods outperformed the standard random selection of the original MLM formulation.

Solving regression problems with interval-valued datasets is a challenging task that may arise in many real world applications. Motivated by that fact, many researchers have proposed nonlinear regression methods to handle interval-valued data in recent years. In this paper, we propose two variants of the Minimal Learning Machine (MLM) for interval-valued data. The choice of MLM is explained by its remarkable performance in many applications and the need of a single hyperparameter definition. We present a performance comparison between our methods and five benchmark nonlinear regression methods. The proposed methods presented competitive results.

Extreme learning machine (ELM) is a recently proposed learning algorithm for single hidden layer feed-foward neural networks (SLFN) that achieved remarkable performances in various applications. In ELM, the hidden neurons are randomly assigned and the output layer weights are learned in a single step using the Moore-Penrose generalized inverse. This approach results in a fast learning neural network algorithm with a single hyperparameter (the number of hidden neurons). Despite the aforementioned advantages, using ELM can result in models with a large number of hidden neurons and this can lead to poor generalization. To overcome this drawback, we propose a novel method to prune hidden layer neurons based on genetic algorithms (GA). The proposed approach, referred as GAP-ELM, selects subset of the hidden neurons to optimize a multiobjective fitness function that defines a compromise between accuracy and the number of pruned neurons. The performance of GAP-ELM is assessed on several real world datasets and compared to other SLFN and a well known pruning method called Optimally Pruned ELM (OP-ELM). On the basis of our experiments, we can state that GAP-ELM is a valid alternative for classification tasks. (C) 2016 Elsevier B.V. All rights reserved.

Ranking is an important task in information retrieval and has gained much attention in recent years. Among the most used strategies, machine learning has achieved important results. The current work proposes a new machine learning based ranking algorithm, the MLM-RANK. MLM-RANK is based on the recently proposed Minimal Learning Machine (MLM). MLM is a supervised learning method that requires the adjustment of a single hyper parameter. The proposed method was evaluated against Prank and ELM Rank, both state of the art point wise ranking methods. In these tests MLM-RANK achieved promising results.

Short-text classification is a challenging natural language processing problem. Beyond classification accuracy, another issue refers to the dimensionality of the feature vectors used for classification. This is especially important for embedded applications with hard constraints of computational power and memory. To deal with such problems, many techniques of dimensionality reduction have been developed over the last years. The Supervised Distance Preserving Projections (SDPP) has shown promising results. This work proposes a modified version of the SDPP method, called Regularized SDPP, which relies on the regularization theory. On the basis of experimental evaluation, the proposed approach has achieved good results in comparison to the state-of-the-art methods in nonlinear dimensionality reduction.