Search results for "Machine"
showing 10 items of 2592 documents
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…
Solid rotor induction machines for use in electrically-assisted turbochargers
2006
The stator leakage reactance has a major influence on the performance of a solid rotor induction machine. It is larger and it is more difficult to define and calculate than in a cage-rotor machine. When the higher leakage reactance is taken into account, the equivalent circuit model gives predictions of sufficient precision for design purposes. The airgap, normally chosen to be quite small, is preferably quite large by general standards. The torque varies as (V/f) 3 which makes it difficult to provide a wide range of constant power operation in a variable speed application. Slitting the rotor helps to improve the characteristic for variable-speed duties.
A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks
2019
In this paper, we apply a new promising tool for pattern classification, namely, the Tsetlin Machine (TM), to the field of disease forecasting. The TM is interpretable because it is based on manipulating expressions in propositional logic, leveraging a large team of Tsetlin Automata (TA). Apart from being interpretable, this approach is attractive due to its low computational cost and its capacity to handle noise. To attack the problem of forecasting, we introduce a preprocessing method that extends the TM so that it can handle continuous input. Briefly stated, we convert continuous input into a binary representation based on thresholding. The resulting extended TM is evaluated and analyzed…
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.
Distributed learning automata for solving a classification task
2016
In this paper, we propose a novel classifier in two-dimensional feature spaces based on the theory of Learning Automata (LA). The essence of our scheme is to search for a separator in the feature space by imposing a LA based random walk in a grid system. To each node in the gird we attach an LA, whose actions are the choice of the edges forming the separator. The walk is self-enclosing, i.e, a new random walk is started whenever the walker returns to starting node forming a closed classification path yielding a many edged polygon. In our approach, the different LA attached at the different nodes search for a polygon that best encircles and separates each class. Based on the obtained polygon…
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.
Implementations of a novel algorithm for colour constancy
1997
AbstractIn agreement with the principles of the relativistic model proposed by Creutzfeldtet al., with the photometric rule (lightness anchoring rule) and with the influence of simultaneous contrast in the appearance of a visual scene, we propose a first-stage mechanism yielding substantial colour constancy. We have defined a set of first-stage colour descriptors, and to test their utility, we have performed a simulation using a Machine Vision System (MVS). The statistical stability of the descriptors for Munsell samples under different illuminants is good.
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.