Search results for "Neural gas"

showing 5 items of 5 documents

Analysis and simulation of creativity learning by means of artificial neural networks

2007

The paper presents a new neural network approach for analysis and simulation of creative behavior. The used concept of Dynamically Controlled Neural Gas (DyCoNG) entails a combination of Dynamically Controlled Network [Perl, J. (2004a). A neural network approach to movement pattern analysis. Human Movement Science,23, 605-620] and Growing Neural Gas (Fritzke, 1995) by quality neurons. A quality neuron reflects the rareness of a piece of information and therefore can measure the originality of a recorded activity that was assigned to the neuron during the network training. The DyCoNG approach was validated using data from a longitudinal field-based study. The creative behavior of 42 particip…

Neural gasProcess (engineering)media_common.quotation_subjectBiophysicsExperimental and Cognitive PsychologyMachine learningcomputer.software_genreNetwork simulationCreativityArtificial IntelligenceHumansLearningComputer SimulationOrthopedics and Sports Medicinecomputer.programming_languagemedia_commonArtificial neural networkbusiness.industryGeneral MedicineCreativityPattern recognition (psychology)Neural Networks ComputerArtificial intelligencePerlbusinessPsychologycomputerNervous system network modelsHuman Movement Science
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Learning to Approach a Moving Ball with a Simulated Two-Wheeled Robot

2006

We show how a two-wheeled robot can learn to approach a moving ball using Reinforcement Learning. The robot is controlled by setting the velocities of its two wheels. It has to reach the ball under certain conditions to be able to kick it towards a given target. In order to kick, the ball has to be in front of the robot. The robot also has to reach the ball at a certain angle in relation to the target, because the ball is always kicked in the direction from the center of the robot to the ball. The robot learns which velocity differences should be applied to the wheels: one of the wheels is set to the maximum velocity, the other one according to this difference. We apply a REINFORCE algorith…

Neural gasRadial basis function networkComputer sciencebusiness.industryRoboticsBang-bang robotComputer Science::RoboticsControl theoryBall (bearing)RobotReinforcement learningArtificial intelligencebusinessSimulation
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Clustering Bacteria Species Using Neural Gas: Preliminary Study

2009

In this work a method for clustering and visualization of bacteria taxonomy is presented. A modified version of the Batch Median Neural Gas (BNG) algorithm is proposed. The BNG algorithm is able to manage non vectorial data given as a dissimilarity matrix. We tested the modified BNG on the dissimilarity matrix obtained from sequences alignment and computing distances using bacteria genotype information regarding the16S rRNA housekeeping gene, which represents a stable part of bacteria genome. The dataset used for the experiments is obtained from the Ribosomal Database Project II, and it is made of 5159 sequences of 16S rRNA genes. Preliminary results of the experiments show a promising abil…

Neural gasbusiness.industryPattern recognitionBiologyRibosomal RNA16S ribosomal RNAcomputer.software_genreGenomeIris flower data setVisualizationHousekeeping geneData miningArtificial intelligenceCluster analysisbusinesscomputer
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Reverse engineering expert visual observations: From fixations to the learning of spatial filters with a neural-gas algorithm

2013

Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give h…

Reverse engineeringNeural gasComputer sciencebusiness.industryProcess (engineering)ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONGeneral Engineeringcomputer.software_genreMachine learningComputer Science ApplicationsTask (project management)Image (mathematics)Artificial IntelligenceEye trackingArtificial intelligenceSet (psychology)businesscomputerAlgorithmExpert Systems with Applications
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ConformalALU: A Conformal Geometric Algebra Coprocessor for Medical Image Processing

2015

Medical imaging involves important computational geometric problems, such as image segmentation and analysis, shape approximation, three-dimensional (3D) modeling, and registration of volumetric data. In the last few years, Conformal Geometric Algebra (CGA), based on five-dimensional (5D) Clifford Algebra, is emerging as a new paradigm that offers simple and universal operators for the representation and solution of complex geometric problems. However, the widespread use of CGA has been so far hindered by its high dimensionality and computational complexity. This paper proposes a simplified formulation of the conformal geometric operations (reflections, rotations, translations, and uniform …

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniCoprocessorComputer scienceClifford algebraConformal geometric algebraConformal mapImage processingParallel computingImage segmentationComputational geometryTheoretical Computer ScienceGeometric algebraOperator (computer programming)Computational Theory and MathematicsConformal geometric algebra five-dimensional clifford algebra computational geometry embedded coprocessors systems-on-programmable-chip FPGA-based prototyping medical imaging segmentation 3D modeling Volume registration Growing Neural Gas marching spheres iterative closest point (ICP) thin-plate spline robust point matching (TPS-RPM)Hardware and ArchitectureScalingSoftwareIEEE Transactions on Computers
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