Search results for "Boosting"
showing 10 items of 59 documents
Mitigation and boosting as face-protection functions
2020
Abstract Mitigation is undeniably and necessarily linked with the social aspect of communication. No speaker mitigates an utterance without a goal in mind, which makes mitigation a means to an end and not an end in itself. Even though the various definitions of mitigation do not assign the same aims to this phenomenon, the social impact it has on the participants in the communication is generally agreed upon throughout the literature (Fraser, 1980; Meyer-Hermann, 1988; Bazzanella et al., 1991; Briz, 1998, 2003; Caffi, 1999; Thaler, 2012; Briz and Albelda, 2013; Schneider, 2013; Albelda et al., 2014; Albelda, 2016, 2018). In this paper, the mitigating and boosting strategies in relationship …
A weighted distance-based approach with boosted decision trees for label ranking
2023
Label Ranking (LR) is an emerging non-standard supervised classification problem with practical applications in different research fields. The Label Ranking task aims at building preference models that learn to order a finite set of labels based on a set of predictor features. One of the most successful approaches to tackling the LR problem consists of using decision tree ensemble models, such as bagging, random forest, and boosting. However, these approaches, coming from the classical unweighted rank correlation measures, are not sensitive to label importance. Nevertheless, in many settings, failing to predict the ranking position of a highly relevant label should be considered more seriou…
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…
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…
Boosting Design Space Explorations with Existing or Automatically Learned Knowledge
2012
During development, processor architectures can be tuned and configured by many different parameters. For benchmarking, automatic design space explorations (DSEs) with heuristic algorithms are a helpful approach to find the best settings for these parameters according to multiple objectives, e.g. performance, energy consumption, or real-time constraints. But if the setup is slightly changed and a new DSE has to be performed, it will start from scratch, resulting in very long evaluation times. To reduce the evaluation times we extend the NSGA-II algorithm in this article, such that automatic DSEs can be supported with a set of transformation rules defined in a highly readable format, the fuz…
Evaluation of Record Linkage Methods for Iterative Insertions
2009
Summary Objectives: There have been many developments and applications of mathematical methods in the context of record linkage as one area of interdisciplinary research efforts. However, comparative evaluations of record linkage methods are still underrepresented. In this paper improvements of the Fellegi-Sunter model are compared with other elaborated classification methods in order to direct further research endeavors to the most promising methodologies. Methods: The task of linking records can be viewed as a special form of object identification. We consider several non-stochastic methods and procedures for the record linkage task in addition to the Fellegi-Sunter model and perform an e…
Learning to Navigate in the Gaussian Mixture Surface
2021
In the last years, deep learning models have achieved remarkable generalization capability on computer vision tasks, obtaining excellent results in fine-grained classification problems. Sophisticated approaches based-on discriminative feature learning via patches have been proposed in the literature, boosting the model performances and achieving the state-of-the-art over well-known datasets. Cross-Entropy (CE) loss function is commonly used to enhance the discriminative power of the deep learned features, encouraging the separability between the classes. However, observing the activation map generated by these models in the hidden layer, we realize that many image regions with low discrimin…
Bagging and Boosting with Dynamic Integration of Classifiers
2000
One approach in classification tasks is to use machine learning techniques to derive classifiers using learning instances. The co-operation of several base classifiers as a decision committee has succeeded to reduce classification error. The main current decision committee learning approaches boosting and bagging use resampling with the training set and they can be used with different machine learning techniques which derive base classifiers. Boosting uses a kind of weighted voting and bagging uses equal weight voting as a combining method. Both do not take into account the local aspects that the base classifiers may have inside the problem space. We have proposed a dynamic integration tech…