Search results for "boost"

DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages

2021

Abstract Automatic Text Complexity Evaluation (ATE) is a research field that aims at creating new methodologies to make autonomous the process of the text complexity evaluation, that is the study of the text-linguistic features (e.g., lexical, syntactical, morphological) to measure the grade of comprehensibility of a text. ATE can affect positively several different contexts such as Finance, Health, and Education. Moreover, it can support the research on Automatic Text Simplification (ATS), a research area that deals with the study of new methods for transforming a text by changing its lexicon and structure to meet specific reader needs. In this paper, we illustrate an ATE approach named De…

Integrating genomic binding site predictions using real-valued meta classifiers

2008

Currently the best algorithms for predicting transcription factor binding sites in DNA sequences are severely limited in accuracy. There is good reason to believe that predictions from different classes of algorithms could be used in conjunction to improve the quality of predictions. In this paper, we apply single layer networks, rules sets, support vector machines and the Adaboost algorithm to predictions from 12 key real valued algorithms. Furthermore, we use a ‘window’ of consecutive results as the input vector in order to contextualise the neighbouring results. We improve the classification result with the aid of under- and over-sampling techniques. We find that support vector machines …

Regularized RBF Networks for Hyperspectral Data Classification

2004

In this paper, we analyze several regularized types of Radial Basis Function (RBF) Networks for crop classification using hyperspectral images. We compare the regularized RBF neural network with Support Vector Machines (SVM) using the RBF kernel, and AdaBoost Regularized (ABR) algorithm using RBF bases, in terms of accuracy and robustness. Several scenarios of increasing input space dimensionality are tested for six images containing six crop classes. Also, regularization, sparseness, and knowledge extraction are paid attention.

Classification of Satellite Images with Regularized AdaBoosting of RBF Neural Networks

2008

ATLAS measurements of the properties of jets for boosted particle searches

2012

Measurements are presented of the properties of high transverse momentum jets, produced in proton-proton collisions at a center-of-mass energy of √s=7  TeV. The data correspond to an integrated luminosity of 35  pb−1 and were collected with the ATLAS detector in 2010. Jet mass, width, eccentricity, planar flow and angularity are measured for jets reconstructed using the anti-kt algorithm with distance parameters R=0.6 and 1.0, with transverse momentum pT>300  GeV and pseudorapidity |η|<2. The measurements are compared to the expectations of Monte Carlo generators that match leading-logarithmic parton showers to leading-order, or next-to-leading-order, matrix elements. The generators describ…

Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide e…

2015

This study aims to compare binary logistic regression (BLR) and stochastic gradient treeboost (SGT) methods in assessing landslide susceptibility within the Mediterranean region for multiple-occurrence regional landslide events. A test area was selected in the north-eastern sector of Sicily (southern Italy) where thousands of debris flows and debris avalanches triggered on the first October 2009 due to an extreme storm. Exploiting the same set of predictors and the 2009 event landslide archive, BLR- and SGT-based susceptibility models have been obtained for the two catchments separately, adopting a random partition (RP) technique for validation. In addition, the models trained in one catchm…

Bidirectional High-Efficiency Nonisolated Step-Up Battery Regulator

2011

The design and results of a high-efficiency high-power (5 kW) nonisolated bidirectional dc-dc converter is presented. High stability due to minimum phase behavior is an additional benefit of the topology. The converter is a new boost with output filter where input and output inductors are coupled. This converter is useful with any system that needs to charge and discharge backup batteries and can be applied in space, automotive, and telecom power systems.

Processing of rock core microtomography images: Using seven different machine learning algorithms

2016

The abilities of machine learning algorithms to process X-ray microtomographic rock images were determined. The study focused on the use of unsupervised, supervised, and ensemble clustering techniques, to segment X-ray computer microtomography rock images and to estimate the pore spaces and pore size diameters in the rocks. The unsupervised k-means technique gave the fastest processing time and the supervised least squares support vector machine technique gave the slowest processing time. Multiphase assemblages of solid phases (minerals and finely grained minerals) and the pore phase were found on visual inspection of the images. In general, the accuracy in terms of porosity values and pore…

Fast prototyping of a SoC-based smart-camera: a real-time fall detection case study

2014

International audience; Smart camera, i.e. cameras that are able to acquire and process images in real-time, is a typical example of the new embedded computer vision systems. A key example of application is automatic fall detection, which can be useful for helping elderly people in daily life. In this paper, we propose a methodology for development and fast-prototyping of a fall detection system based on such a smart camera, which allows to reduce the development time compared to standard approaches. Founded on a supervised classification approach, we propose a HW/SW implementation to detect falls in a home environment using a single camera and an optimized descriptor adapted to real-time t…

Alternating model trees

2015

Model tree induction is a popular method for tackling regression problems requiring interpretable models. Model trees are decision trees with multiple linear regression models at the leaf nodes. In this paper, we propose a method for growing alternating model trees, a form of option tree for regression problems. The motivation is that alternating decision trees achieve high accuracy in classification problems because they represent an ensemble classifier as a single tree structure. As in alternating decision trees for classification, our alternating model trees for regression contain splitter and prediction nodes, but we use simple linear regression functions as opposed to constant predicto…