Search results for "machine learning."
showing 10 items of 1455 documents
Testing for non-linearity in an artificial financial market: a recurrence quantification approach
2004
Abstract In this paper, earlier work on testing for non-linear dynamics on realizations from an artificial financial market is extended in two ways. On the one hand, Hinich’s bispectral test and White’s neural network test are computed. On the other hand, a recently developed methodology to test for hidden structures in data, inherited from Physics, is successfully applied on the realizations of the artificial market. Results among alternative tests are compared.
Boosting organizational learning through team-based talent management: what is the evidence from large Spanish firms?
2013
Talent management (TM) can crucially help optimize organizational learning (OL) processes. The aim of this article is to study whether certain TM practices related to teamwork design and dynamics stimulate and develop learning (i.e. knowledge creation) processes within the organization and across the different ontological levels (individual, group and organizational–institutional). A model linking team-based TM and OL is tested in a sample of large Spanish companies. Our empirical results emphasize the distinction between individual–group and institutional levels of learning as the two pillars of OL. The results also highlight the role of team autonomy and creativity as crucial factors for …
Using Cellular Automata for feature construction - preliminary study
2007
When first faced with a learning task, it is often not clear what a good representation of the training data should look like. We are often forced to create some set of features that appear plausible, without any strong confidence that they will yield superior learning. Beside, we often do not have any prior knowledge of what learning method is the best to apply, and thus often try multiple methods in an attempt to find the one that performs best. This paper describes a new method and its preliminary study for constructing features based on cellular automata (CA). Our approach uses self-organisation ability of cellular automata by constructing features being most efficient for making predic…
Target Classification by mmWave FMCW Radars Using Machine Learning on Range-Angle Images
2021
In this paper, we present a novel multiclass-target classification method for mmWave frequency modulated continuous wave (FMCW) radar operating in the frequency range of 77 - 81 GHz, based on custom range-angle heatmaps and machine learning tools. The elevation field of view (FoV) is increased by orienting the Radar antennas in elevation. In this orientation, the radar focuses the beam in elevation to improve the elevation FoV. The azimuth FoV is improved by mechanically rotating the Radar horizontally, which has antenna elements oriented in the elevation direction. The data from the Radar measurements obtained by mechanical rotation of the Radar in Azimuth are used to generate a range-angl…
Manipulating the alpha level cannot cure significance testing
2018
We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple in…
Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis
2016
Physically-based radiative transfer models (RTMs) help understand the interactions of radiation with vegetation and atmosphere. However, advanced RTMs can be computationally burdensome, which makes them impractical in many real applications, especially when many state conditions and model couplings need to be studied. To overcome this problem, it is proposed to substitute RTMs through surrogate meta-models also named emulators. Emulators approximate the functioning of RTMs through statistical learning regression methods, and can open many new applications because of their computational efficiency and outstanding accuracy. Emulators allow fast global sensitivity analysis (GSA) studies on adv…
Responsive and Minimalist App Based on Explainable AI to Assess Palliative Care Needs during Bedside Consultations on Older Patients
2021
[EN] Palliative care is an alternative to standard care for gravely ill patients that has demonstrated many clinical benefits in cost-effective interventions. It is expected to grow in demand soon, so it is necessary to detect those patients who may benefit from these programs using a personalised objective criterion at the correct time. Our goal was to develop a responsive and minimalist web application embedding a 1-year mortality explainable predictive model to assess palliative care at bedside consultation. A 1-year mortality predictive model has been trained. We ranked the input variables and evaluated models with an increasing number of variables. We selected the model with the seven …
Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies
2018
We consider multiobjective optimization problems where objective functions have different (or heterogeneous) evaluation times or latencies. This is of great relevance for (computationally) expensive multiobjective optimization as there is no reason to assume that all objective functions should take an equal amount of time to be evaluated (particularly when objectives are evaluated separately). To cope with such problems, we propose a variation of the Kriging-assisted reference vector guided evolutionary algorithm (K-RVEA) called heterogeneous K-RVEA (short HK-RVEA). This algorithm is a merger of two main concepts designed to account for different latencies: A single-objective evolutionary a…
On Dealing with Uncertainties from Kriging Models in Offline Data-Driven Evolutionary Multiobjective Optimization
2019
Many works on surrogate-assisted evolutionary multiobjective optimization have been devoted to problems where function evaluations are time-consuming (e.g., based on simulations). In many real-life optimization problems, mathematical or simulation models are not always available and, instead, we only have data from experiments, measurements or sensors. In such cases, optimization is to be performed on surrogate models built on the data available. The main challenge there is to fit an accurate surrogate model and to obtain meaningful solutions. We apply Kriging as a surrogate model and utilize corresponding uncertainty information in different ways during the optimization process. We discuss…
Emergency Analysis: Multitask Learning with Deep Convolutional Neural Networks for Fire Emergency Scene Parsing
2021
In this paper, we introduce a novel application of using scene semantic image segmentation for fire emergency situation analysis. To analyse a fire emergency scene, we propose to use deep convolutional image segmentation networks to identify and classify objects in a scene based on their build material and their vulnerability to catch fire. We introduce our own fire emergency scene segmentation dataset for this purpose. It consists of real world images with objects annotated on the basis of their build material. We use state-of-the-art segmentation models: DeepLabv3, DeepLabv3+, PSPNet, FCN, SegNet and UNet to compare and evaluate their performance on the fire emergency scene parsing task. …