Search results for "machine learning."
showing 10 items of 1455 documents
Feature Ranking of Large, Robust, and Weighted Clustering Result
2017
A clustering result needs to be interpreted and evaluated for knowledge discovery. When clustered data represents a sample from a population with known sample-to-population alignment weights, both the clustering and the evaluation techniques need to take this into account. The purpose of this article is to advance the automatic knowledge discovery from a robust clustering result on the population level. For this purpose, we derive a novel ranking method by generalizing the computation of the Kruskal-Wallis H test statistic from sample to population level with two different approaches. Application of these enlargements to both the input variables used in clustering and to metadata provides a…
Assessment of workflow feature selection on forest LAI prediction with sentinel-2A MSI, landsat 7 ETM+ and Landsat 8 OLI
2020
The European Space Agency (ESA)’s Sentinel-2A (S2A) mission is providing time series that allow the characterisation of dynamic vegetation, especially when combined with the National Aeronautics and Space Administration (NASA)/United States Geological Survey (USGS) Landsat 7 (L7) and Landsat 8 (L8) missions. Hybrid retrieval workflows combining non-parametric Machine Learning Regression Algorithms (MLRAs) and vegetation Radiative Transfer Models (RTMs) were proposed as fast and accurate methods to infer biophysical parameters such as Leaf Area Index (LAI) from these data streams. However, the exact design of optimal retrieval workflows is rarely discussed. In this study, the impact of…
Explainable Reinforcement Learning with the Tsetlin Machine
2021
The Tsetlin Machine is a recent supervised machine learning algorithm that has obtained competitive results in several benchmarks, both in terms of accuracy and resource usage. It has been used for convolution, classification, and regression, producing interpretable rules. In this paper, we introduce the first framework for reinforcement learning based on the Tsetlin Machine. We combined the value iteration algorithm with the regression Tsetlin Machine, as the value function approximator, to investigate the feasibility of training the Tsetlin Machine through bootstrapping. Moreover, we document robustness and accuracy of learning on several instances of the grid-world problem.
Generalized Bayesian Pursuit: A Novel Scheme for Multi-Armed Bernoulli Bandit Problems
2011
In the last decades, a myriad of approaches to the multi-armed bandit problem have appeared in several different fields. The current top performing algorithms from the field of Learning Automata reside in the Pursuit family, while UCB-Tuned and the e-greedy class of algorithms can be seen as state-of-the-art regret minimizing algorithms. Recently, however, the Bayesian Learning Automaton (BLA) outperformed all of these, and other schemes, in a wide range of experiments. Although seemingly incompatible, in this paper we integrate the foundational learning principles motivating the design of the BLA, with the principles of the so-called Generalized Pursuit algorithm (GPST), leading to the Gen…
Using Learning Automata to Enhance Local-Search Based SAT Solvers with Learning Capability
2010
In this work, we have introduced a new approach based on combining Learning Automata with Random Walk and GSAT w/Random Walk. In order to get a comprehensive overview of the new algorithms' performance, we used a set of benchmark problems containing different problems from various domains. In these benchmark problems, both RW and GSATRW suffers from stagnation behaviour which directly affects their performance. This phenomenon is, however, only observed for LA-GSATRW on the largest problem instances. Finally, the
Review on Machine Learning Based Lesion Segmentation Methods from Brain MR Images
2016
Brain lesions are life threatening diseases. Traditional diagnosis of brain lesions is performed visually by neuro-radiologists. Nowadays, advanced technologies and the progress in magnetic resonance imaging provide computer aided diagnosis using automated methods that can detect and segment abnormal regions from different medical images. Among several techniques, machine learning based methods are flexible and efficient. Therefore, in this paper, we present a review on techniques applied for detection and segmentation of brain lesions from magnetic resonance images with supervised and unsupervised machine learning techniques.
Learning vector quantization with alternative distance criteria
2003
An adaptive algorithm for training of a nearest neighbour (NN) classifier is developed in this paper. This learning rule has some similarity to the well-known LVQ method, but uses the nearest centroid neighbourhood concept to estimate optimal locations of the codebook vectors. The aim of this approach is to improve the performance of the standard LVQ algorithms when using a very small codebook. The behaviour of the learning technique proposed here is experimentally compared to those of the plain k-NN decision rule and the LVQ algorithms.
Las funciones interactivas del marcador español ‘¿no?’ Las fronteras entre la atenuación y la protección de la imagen
2020
In this paper the functions of the Spanish discourse marker ?no? are analysed from a pragmatic and an interactive perspective. Specifically, we explore the values of ?no? taking the pragmatic phenomena of mitigation and boosting, as well as the notion of affiliation as described in conversation analysis. The previous literature devoted to the study of this linguistic form has consistently identified its uses as a confirmation request or a phatic device (Fuentes, 1990, 2009; Santos Rio, 2003; Garcia Vizcaino, 2005; Montanez, 2008, 2015; Rodriguez Munoz, 2009; Moccero, 2010; Santana, 2017). This work, however, analyses how the mitigating uses interact and share features with neighbouring cate…
Modeling Listeners’ Emotional Response to Music
2012
An overview of the computational prediction of emotional responses to music is presented. Communication of emotions by music has received a great deal of attention during the last years and a large number of empirical studies have described the role of individual features (tempo, mode, articulation, timbre) in predicting the emotions suggested or invoked by the music. However, unlike the present work, relatively few studies have attempted to model continua of expressed emotions using a variety of musical features from audio-based representations in a correlation design. The construction of the computational model is divided into four separate phases, with a different focus for evaluation. T…
Les eines computacionals i el disseny de corpus orals: un diàleg vigent
2020
The design of an oral corpus and the processes of registering, codifying and treating the materials in order to build a useful resource for linguistic analysis prompt numerous decisions regarding theory and methodology. This article is focused on those stages of corpus construction which are more clearly conditioned by the computational processing necessary to make it functional. In order to adequately match the initial expectations and the real possibilities of using the tool, each feature we intend to codify must be measured against the workload and the means required to do so. Therefore, it is essential to take into account the available possibilities of processing and exploitation as th…