An Incremental Probabilistic Neural Network for Regression and Reinforcement Learning Tasks

Heinen, Milton Roberto; Engel, Paulo Martins

doi:10.1007/978-3-642-15822-3_22

Cited by 15 publications

(17 citation statements)

References 14 publications

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“…The method chosen to learn it is an incremental version of multivariate GMM [9]. By feeding the algorithm with the data samples as they arrive from the sensors, this method learns while the robot is moving, and as it is incremental, after a few seconds gives good predictions for common situations, e.g.…”

Section: B Definition Of Our Modelmentioning

confidence: 99%

“…As described by [9], both the learning algorithm and prediction algorithm compute the likelihoods of hundreds of multivariate normal distributions. In our case, we set a threshold on the minimum mass that a component needs to incorporate in order to be used as predictor, so very young components or spurious ones are not used.…”

Section: Learning and Prediction Using The Gmmmentioning

confidence: 99%

“…However, learning does compute likelihoods for every component, as it is necessary for computing posterior probabilities. [9] show the update equations for the mixture components, which basically add a term to the mean and covariances, weighted by the proportion in which the sample's mass con- Fig. 4.…”

Section: Learning and Prediction Using The Gmmmentioning

confidence: 99%

“…The choice of one predictor or another should be made in terms of how the data is distributed. Moreover, we use a generic state-of-the-art incremental online learning algorithm [9] for the task of building a model to predict the optical flow perceived by a mobile robot. Finally, as an application, the model is also used to learn a simple predictor for anticipating an imminent collision.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Incremental learning of an optical flow model for sensorimotor anticipation in a mobile robot

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Demiris

et al. 2012

2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL)

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Abstract-In order to anticipate dangerous events, like a collision, an agent needs to make long-term predictions. However, those are challenging due to uncertainties in internal and external variables and environment dynamics. A sensorimotor model is acquired online by the mobile robot using a state-of-the-art method that learns the optical flow distribution in images, both in space and time. The learnt model is used to anticipate the optical flow up to a given time horizon and to predict an imminent collision by using reinforcement learning. We demonstrate that multi-modal predictions reduce to simpler distributions once actions are taken into account.

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Section: B Definition Of Our Modelmentioning

confidence: 99%

Section: Learning and Prediction Using The Gmmmentioning

confidence: 99%

Section: Learning and Prediction Using The Gmmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Incremental learning of an optical flow model for sensorimotor anticipation in a mobile robot

Ribes

Cerquides

Demiris

et al. 2012

2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL)

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“…An important issue in unsupervised incremental learning is the stability-plasticity dilemma, i.e., whether a new presented data point must be assimilated in the current model or cause a structural change in the model to accommodate the new information that it bears, i.e., a new concept. We show that our algorithm, the so called IGMM (standing for Incremental Gaussian Mixture Model), uses a probabilistic approach for modeling the environment, and so, it can rely on solid arguments to handle this issue [5,6].…”

Section: Introductionmentioning

confidence: 99%

Concept Formation Using Incremental Gaussian Mixture Models

Engel

Heinen

2010

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. This paper presents a new algorithm for incremental concept formation based on a Bayesian framework. The algorithm, called IGMM (for Incremental Gaussian Mixture Model), uses a probabilistic approach for modeling the environment, and so, it can rely on solid arguments to handle this issue. IGMM creates and continually adjusts a probabilistic model consistent to all sequentially presented data without storing or revisiting previous training data. IGMM is particularly useful for incremental clustering of data streams, as encountered in the domain of moving object trajectories and mobile robotics. It creates an incremental knowledge model of the domain consisting of primitive concepts involving all observed variables. Experiments with simulated data streams of sonar readings of a mobile robot shows that IGMM can efficiently segment trajectories detecting higher order concepts like "wall at right" and "curve at left".

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Probabilistic neural network training procedure based on Q(0)-learning algorithm in medical data classification

Kusy

Zajdel

2014

Appl Intell

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In this article, an iterative procedure is proposed for the training process of the probabilistic neural network (PNN). In each stage of this procedure, the Q(0)-learning algorithm is utilized for the adaptation of PNN smoothing parameter (σ). Four classes of PNN models are regarded in this study. In the case of the first, simplest model, the smoothing parameter takes the form of a scalar; for the second model, σ is a vector whose elements are computed with respect to the class index; the third considered model has the smoothing parameter vector for which all components are determined depending on each input attribute; finally, the last and the most complex of the analyzed networks, uses the matrix of smoothing parameters where each element is dependent on both class and input feature index. The main idea of the presented approach is based on the appropriate update of the smoothing parameter values according to the Q(0)-learning algorithm. The proposed procedure is verified on six repository data sets. The prediction ability of the algorithm is assessed by computing the test accuracy on 10 %, 20 %, 30 %, and 40 % of examples drawn randomly from each input data set. The results are compared with the test accuracy obtained by PNN trained using the conjugate gradient procedure, support vector machine algorithm, gene expression programming classifier, k-Means method, multilayer perceptron, radial basis function neural network and learning vector quantization neural network. It is shown M. Kusy () • R. Zajdel Faculty of Electrical and Computer Engineering,

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An Incremental Probabilistic Neural Network for Regression and Reinforcement Learning Tasks

Cited by 15 publications

References 14 publications

Incremental learning of an optical flow model for sensorimotor anticipation in a mobile robot

Incremental learning of an optical flow model for sensorimotor anticipation in a mobile robot

Concept Formation Using Incremental Gaussian Mixture Models

Probabilistic neural network training procedure based on Q(0)-learning algorithm in medical data classification

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