Search results for "Boosting"

Beyond the surface of media disruption: digital technology boosting new business logics, professional practices and entrepreneurial identities

2019

Digital disruption has recently been one of the main focal points of media management scholars. Digitalisation is regarded as the main driver of change that results in enormous managerial and organ...

Comparing Boosting and Bagging for Decision Trees of Rankings

2021

AbstractDecision tree learning is among the most popular and most traditional families of machine learning algorithms. While these techniques excel in being quite intuitive and interpretable, they also suffer from instability: small perturbations in the training data may result in big changes in the predictions. The so-called ensemble methods combine the output of multiple trees, which makes the decision more reliable and stable. They have been primarily applied to numeric prediction problems and to classification tasks. In the last years, some attempts to extend the ensemble methods to ordinal data can be found in the literature, but no concrete methodology has been provided for preference…

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 …

Combining Real-Time Segmentation and Classification of Rehabilitation Exercises with LSTM Networks and Pointwise Boosting

2020

Autonomous biofeedback tools in support of rehabilitation patients are commonly built as multi-tier pipelines, where a segmentation algorithm is first responsible for isolating motion primitives, and then classification can be performed on each primitive. In this paper, we present a novel segmentation technique that integrates on-the-fly qualitative classification of physical movements in the process. We adopt Long Short-Term Memory (LSTM) networks to model the temporal patterns of a streaming multivariate time series, obtained by sampling acceleration and angular velocity of the limb in motion, and then we aggregate the pointwise predictions of each isolated movement using different boosti…

Cluster-Localized Sparse Logistic Regression for SNP Data

2012

The task of analyzing high-dimensional single nucleotide polymorphism (SNP) data in a case-control design using multivariable techniques has only recently been tackled. While many available approaches investigate only main effects in a high-dimensional setting, we propose a more flexible technique, cluster-localized regression (CLR), based on localized logistic regression models, that allows different SNPs to have an effect for different groups of individuals. Separate multivariable regression models are fitted for the different groups of individuals by incorporating weights into componentwise boosting, which provides simultaneous variable selection, hence sparse fits. For model fitting, th…

Coupled variable selection for regression modeling of complex treatment patterns in a clinical cancer registry.

2013

For determining a manageable set of covariates potentially influential with respect to a time-to-event endpoint, Cox proportional hazards models can be combined with variable selection techniques, such as stepwise forward selection or backward elimination based on p-values, or regularized regression techniques such as component-wise boosting. Cox regression models have also been adapted for dealing with more complex event patterns, for example, for competing risks settings with separate, cause-specific hazard models for each event type, or for determining the prognostic effect pattern of a variable over different landmark times, with one conditional survival model for each landmark. Motivat…

Adaptive sparse representation of continuous input for tsetlin machines based on stochastic searching on the line

2021

This paper introduces a novel approach to representing continuous inputs in Tsetlin Machines (TMs). Instead of using one Tsetlin Automaton (TA) for every unique threshold found when Booleanizing continuous input, we employ two Stochastic Searching on the Line (SSL) automata to learn discriminative lower and upper bounds. The two resulting Boolean features are adapted to the rest of the clause by equipping each clause with its own team of SSLs, which update the bounds during the learning process. Two standard TAs finally decide whether to include the resulting features as part of the clause. In this way, only four automata altogether represent one continuous feature (instead of potentially h…

Boosting working memory with accelerated clocks

2021

Our perception of time varies with the degree of cognitive engagement in tasks. The perceived passage of time accelerates while working on demanding tasks, whereas time appears to drag during boring situations. Our experiment aimed at investigating whether this relationship is mutual: Can manipulated announcements of elapsed time systematically affect the attentional resources applied to a cognitive task? We measured behavioral performance and the EEG in a whole report working memory paradigm with six items of different colors that each had to be reported after a short delay period. The 32 participants were informed about the current time after each 20 trials, while the clock was running at…

Cover Feature: Boosting the Supercapacitive Behavior of CoAl Layered Double Hydroxides via Tuning the Metal Composition and Interlayer Space (Batteri…

2020

Increase in rear-end collision risk by acute stress-induced fatigue in on-road truck driving.

2021

Increasing road crashes related to occupational drivers’ deteriorating health has become a social problem. To prevent road crashes, warnings and predictions of increased crash risk based on drivers’ conditions are important. However, in on-road driving, the relationship between drivers’ physiological condition and crash risk remains unclear due to difficulties in the simultaneous measurement of both. This study aimed to elucidate the relationship between drivers’ physiological condition assessed by autonomic nerve function (ANF) and an indicator of rear-end collision risk in on-road driving. Data from 20 male truck drivers (mean ± SD, 49.0±8.2 years; range, 35–63 years) were analyzed. Over …