Academic literature on the topic 'Non-parametric learning'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Non-parametric learning.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Non-parametric learning"
1
Liu, Bing, Shi-Xiong Xia, and Yong Zhou. "Unsupervised non-parametric kernel learning algorithm." Knowledge-Based Systems 44 (May 2013): 1–9. http://dx.doi.org/10.1016/j.knosys.2012.12.008.
Full text
2
Esser, Pascal, Maximilian Fleissner, and Debarghya Ghoshdastidar. "Non-parametric Representation Learning with Kernels." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (March 24, 2024): 11910–18. http://dx.doi.org/10.1609/aaai.v38i11.29077.
Full textAbstract:
Unsupervised and self-supervised representation learning has become popular in recent years for learning useful features from unlabelled data. Representation learning has been mostly developed in the neural network literature, and other models for representation learning are surprisingly unexplored. In this work, we introduce and analyze several kernel-based representation learning approaches: Firstly, we define two kernel Self-Supervised Learning (SSL) models using contrastive loss functions and secondly, a Kernel Autoencoder (AE) model based on the idea of embedding and reconstructing data. We argue that the classical representer theorems for supervised kernel machines are not always applicable for (self-supervised) representation learning, and present new representer theorems, which show that the representations learned by our kernel models can be expressed in terms of kernel matrices. We further derive generalisation error bounds for representation learning with kernel SSL and AE, and empirically evaluate the performance of these methods in both small data regimes as well as in comparison with neural network based models.
3
Cruz, David Luviano, Francesco José García Luna, and Luis Asunción Pérez Domínguez. "Multiagent reinforcement learning using Non-Parametric Approximation." Respuestas 23, no. 2 (July 1, 2018): 53–61. http://dx.doi.org/10.22463/0122820x.1738.
Full textAbstract:
This paper presents a hybrid control proposal for multi-agent systems, where the advantages of the reinforcement learning and nonparametric functions are exploited. A modified version of the Q-learning algorithm is used which will provide data training for a Kernel, this approach will provide a sub optimal set of actions to be used by the agents. The proposed algorithm is experimentally tested in a path generation task in an unknown environment for mobile robots.
4
Khadse, Vijay M., Parikshit Narendra Mahalle, and Gitanjali R. Shinde. "Statistical Study of Machine Learning Algorithms Using Parametric and Non-Parametric Tests." International Journal of Ambient Computing and Intelligence 11, no. 3 (July 2020): 80–105. http://dx.doi.org/10.4018/ijaci.2020070105.
Full textAbstract:
The emerging area of the internet of things (IoT) generates a large amount of data from IoT applications such as health care, smart cities, etc. This data needs to be analyzed in order to derive useful inferences. Machine learning (ML) plays a significant role in analyzing such data. It becomes difficult to select optimal algorithm from the available set of algorithms/classifiers to obtain best results. The performance of algorithms differs when applied to datasets from different application domains. In learning, it is difficult to understand if the difference in performance is real or due to random variation in test data, training data, or internal randomness of the learning algorithms. This study takes into account these issues during a comparison of ML algorithms for binary and multivariate classification. It helps in providing guidelines for statistical validation of results. The results obtained show that the performance measure of accuracy for one algorithm differs by critical difference (CD) than others over binary and multivariate datasets obtained from different application domains.
5
Yoa, Seungdong, Jinyoung Park, and Hyunwoo J. Kim. "Learning Non-Parametric Surrogate Losses With Correlated Gradients." IEEE Access 9 (2021): 141199–209. http://dx.doi.org/10.1109/access.2021.3120092.
Full text
6
Rutkowski, Leszek. "Non-parametric learning algorithms in time-varying environments." Signal Processing 18, no. 2 (October 1989): 129–37. http://dx.doi.org/10.1016/0165-1684(89)90045-5.
Full text
7
Liu, Mingming, Bing Liu, Chen Zhang, and Wei Sun. "Embedded non-parametric kernel learning for kernel clustering." Multidimensional Systems and Signal Processing 28, no. 4 (August 10, 2016): 1697–715. http://dx.doi.org/10.1007/s11045-016-0440-1.
Full text
8
Chen, Changyou, Junping Zhang, Xuefang He, and Zhi-Hua Zhou. "Non-Parametric Kernel Learning with robust pairwise constraints." International Journal of Machine Learning and Cybernetics 3, no. 2 (September 17, 2011): 83–96. http://dx.doi.org/10.1007/s13042-011-0048-6.
Full text
9
Kaur, Navdeep, Gautam Kunapuli, and Sriraam Natarajan. "Non-parametric learning of lifted Restricted Boltzmann Machines." International Journal of Approximate Reasoning 120 (May 2020): 33–47. http://dx.doi.org/10.1016/j.ijar.2020.01.003.
Full text
10
Wang, Mingyang, Zhenshan Bing, Xiangtong Yao, Shuai Wang, Huang Kai, Hang Su, Chenguang Yang, and Alois Knoll. "Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (June 26, 2023): 10157–65. http://dx.doi.org/10.1609/aaai.v37i8.26210.
Full textAbstract:
Meta-reinforcement learning enables artificial agents to learn from related training tasks and adapt to new tasks efficiently with minimal interaction data. However, most existing research is still limited to narrow task distributions that are parametric and stationary, and does not consider out-of-distribution tasks during the evaluation, thus, restricting its application. In this paper, we propose MoSS, a context-based Meta-reinforcement learning algorithm based on Self-Supervised task representation learning to address this challenge. We extend meta-RL to broad non-parametric task distributions which have never been explored before, and also achieve state-of-the-art results in non-stationary and out-of-distribution tasks. Specifically, MoSS consists of a task inference module and a policy module. We utilize the Gaussian mixture model for task representation to imitate the parametric and non-parametric task variations. Additionally, our online adaptation strategy enables the agent to react at the first sight of a task change, thus being applicable in non-stationary tasks. MoSS also exhibits strong generalization robustness in out-of-distributions tasks which benefits from the reliable and robust task representation. The policy is built on top of an off-policy RL algorithm and the entire network is trained completely off-policy to ensure high sample efficiency. On MuJoCo and Meta-World benchmarks, MoSS outperforms prior works in terms of asymptotic performance, sample efficiency (3-50x faster), adaptation efficiency, and generalization robustness on broad and diverse task distributions.
Dissertations / Theses on the topic "Non-parametric learning"
1
Zewdie, Dawit (Dawit Habtamu). "Representation discovery in non-parametric reinforcement learning." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91883.
Full textAbstract:
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 71-73).
Recent years have seen a surge of interest in non-parametric reinforcement learning. There are now practical non-parametric algorithms that use kernel regression to approximate value functions. The correctness guarantees of kernel regression require that the underlying value function be smooth. Most problems of interest do not satisfy this requirement in their native space, but can be represented in such a way that they do. In this thesis, we show that the ideal representation is one that maps points directly to their values. Existing representation discovery algorithms that have been used in parametric reinforcement learning settings do not, in general, produce such a representation. We go on to present Fit-Improving Iterative Representation Adjustment (FIIRA), a novel framework for function approximation and representation discovery, which interleaves steps of value estimation and representation adjustment to increase the expressive power of a given regression scheme. We then show that FIIRA creates representations that correlate highly with value, giving kernel regression the power to represent discontinuous functions. Finally, we extend kernel-based reinforcement learning to use FIIRA and show that this results in performance improvements on three benchmark problems: Mountain-Car, Acrobot, and PinBall.
by Dawit Zewdie.
M. Eng.
2
Campanholo, Guizilini Vitor. "Non-Parametric Learning for Monocular Visual Odometry." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9903.
Full textAbstract:
This thesis addresses the problem of incremental localization from visual information, a scenario commonly known as visual odometry. Current visual odometry algorithms are heavily dependent on camera calibration, using a pre-established geometric model to provide the transformation between input (optical flow estimates) and output (vehicle motion estimates) information. A novel approach to visual odometry is proposed in this thesis where the need for camera calibration, or even for a geometric model, is circumvented by the use of machine learning principles and techniques. A non-parametric Bayesian regression technique, the Gaussian Process (GP), is used to elect the most probable transformation function hypothesis from input to output, based on training data collected prior and during navigation. Other than eliminating the need for a geometric model and traditional camera calibration, this approach also allows for scale recovery even in a monocular configuration, and provides a natural treatment of uncertainties due to the probabilistic nature of GPs. Several extensions to the traditional GP framework are introduced and discussed in depth, and they constitute the core of the contributions of this thesis to the machine learning and robotics community. The proposed framework is tested in a wide variety of scenarios, ranging from urban and off-road ground vehicles to unconstrained 3D unmanned aircrafts. The results show a significant improvement over traditional visual odometry algorithms, and also surpass results obtained using other sensors, such as laser scanners and IMUs. The incorporation of these results to a SLAM scenario, using a Exact Sparse Information Filter (ESIF), is shown to decrease global uncertainty by exploiting revisited areas of the environment. Finally, a technique for the automatic segmentation of dynamic objects is presented, as a way to increase the robustness of image information and further improve visual odometry results.
3
Bratières, Sébastien. "Non-parametric Bayesian models for structured output prediction." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274973.
Full textAbstract:
Structured output prediction is a machine learning tasks in which an input object is not just assigned a single class, as in classification, but multiple, interdependent labels. This means that the presence or value of a given label affects the other labels, for instance in text labelling problems, where output labels are applied to each word, and their interdependencies must be modelled. Non-parametric Bayesian (NPB) techniques are probabilistic modelling techniques which have the interesting property of allowing model capacity to grow, in a controllable way, with data complexity, while maintaining the advantages of Bayesian modelling. In this thesis, we develop NPB algorithms to solve structured output problems. We first study a map-reduce implementation of a stochastic inference method designed for the infinite hidden Markov model, applied to a computational linguistics task, part-of-speech tagging. We show that mainstream map-reduce frameworks do not easily support highly iterative algorithms. The main contribution of this thesis consists in a conceptually novel discriminative model, GPstruct. It is motivated by labelling tasks, and combines attractive properties of conditional random fields (CRF), structured support vector machines, and Gaussian process (GP) classifiers. In probabilistic terms, GPstruct combines a CRF likelihood with a GP prior on factors; it can also be described as a Bayesian kernelized CRF. To train this model, we develop a Markov chain Monte Carlo algorithm based on elliptical slice sampling and investigate its properties. We then validate it on real data experiments, and explore two topologies: sequence output with text labelling tasks, and grid output with semantic segmentation of images. The latter case poses scalability issues, which are addressed using likelihood approximations and an ensemble method which allows distributed inference and prediction. The experimental validation demonstrates: (a) the model is flexible and its constituent parts are modular and easy to engineer; (b) predictive performance and, most crucially, the probabilistic calibration of predictions are better than or equal to that of competitor models, and (c) model hyperparameters can be learnt from data.
4
Prando, Giulia. "Non-Parametric Bayesian Methods for Linear System Identification." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3426195.
Full textAbstract:
Recent contributions have tackled the linear system identification problem by means of non-parametric Bayesian methods, which are built on largely adopted machine learning techniques, such as Gaussian Process regression and kernel-based regularized regression. Following the Bayesian paradigm, these procedures treat the impulse response of the system to be estimated as the realization of a Gaussian process. Typically, a Gaussian prior accounting for stability and smoothness of the impulse response is postulated, as a function of some parameters (called hyper-parameters in the Bayesian framework). These are generally estimated by maximizing the so-called marginal likelihood, i.e. the likelihood after the impulse response has been marginalized out. Once the hyper-parameters have been fixed in this way, the final estimator is computed as the conditional expected value of the impulse response w.r.t. the posterior distribution, which coincides with the minimum variance estimator. Assuming that the identification data are corrupted by Gaussian noise, the above-mentioned estimator coincides with the solution of a regularized estimation problem, in which the regularization term is the l2 norm of the impulse response, weighted by the inverse of the prior covariance function (a.k.a. kernel in the machine learning literature).
Recent works have shown how such Bayesian approaches are able to jointly perform estimation and model selection, thus overcoming one of the main issues affecting parametric identification procedures, that is complexity selection.
While keeping the classical system identification methods (e.g. Prediction Error Methods and subspace algorithms) as a benchmark for numerical comparison, this thesis extends and analyzes some key aspects of the above-mentioned Bayesian procedure. In particular, four main topics are considered.
1. PRIOR DESIGN. Adopting Maximum Entropy arguments, a new type of l2 regularization is derived: the aim is to penalize the rank of the block Hankel matrix built with Markov coefficients, thus controlling the complexity of the identified model, measured by its McMillan degree. By accounting for the coupling between different input-output channels, this new prior results particularly suited when dealing for the identification of MIMO systems
To speed up the computational requirements of the estimation algorithm, a tailored version of the Scaled Gradient Projection algorithm is designed to optimize the marginal likelihood.
2. CHARACTERIZATION OF UNCERTAINTY. The confidence sets returned by the non-parametric Bayesian identification algorithm are analyzed and compared with those returned by parametric Prediction Error Methods. The comparison is carried out in the impulse response space, by deriving “particle” versions (i.e. Monte-Carlo approximations) of the standard confidence sets.
3. ONLINE ESTIMATION. The application of the non-parametric Bayesian system identification techniques is extended to an online setting, in which new data become available as time goes. Specifically, two key modifications of the original “batch” procedure are proposed in order to meet the real-time requirements. In addition, the identification of time-varying systems is tackled by introducing a forgetting factor in the estimation criterion and by treating it as a hyper-parameter.
4. POST PROCESSING: MODEL REDUCTION. Non-parametric Bayesian identification procedures estimate the unknown system in terms of its impulse response coefficients, thus returning a model with high (possibly infinite) McMillan degree. A tailored procedure is proposed to reduce such model to a lower degree one, which appears more suitable for filtering and control applications. Different criteria for the selection of the order of the reduced model are evaluated and compared.Recentemente, il problema di identificazione di sistemi lineari è stato risolto ricorrendo a metodi Bayesiani non-parametrici, che sfruttano di tecniche di Machine Learning ampiamente utilizzate, come la regressione gaussiana e la regolarizzazione basata su kernels. Seguendo il paradigma Bayesiano, queste procedure richiedono una distribuzione Gaussiana a-priori per la risposta impulsiva. Tale distribuzione viene definita in funzione di alcuni parametri (chiamati iper-parametri nell'ambito Bayesiano), che vengono stimati usando i dati a disposizione. Una volta che gli iper-parametri sono stati fissati, è possibile calcolare lo stimatore a minima varianza come il valore atteso della risposta impulsiva, condizionato rispetto alla distribuzione a posteriori. Assumendo che i dati di identificazione siano corrotti da rumore Gaussiano, tale stimatore coincide con la soluzione di un problema di stima regolarizzato, nel quale il termine di regolarizzazione è la norma l2 della risposta impulsiva, pesata dall'inverso della funzione di covarianza a priori (tale funzione viene anche detta "kernel" nella letteratura di Machine Learning). Recenti lavori hanno dimostrato come questi metodi Bayesiani possano contemporaneamente selezionare un modello ottimale e stimare la quantità sconosciuta. In tal modo sono in grado di superare uno dei principali problemi che affliggono le tecniche di identificazione parametrica, ovvero quella della selezione della complessità di modello. Considerando come benchmark le tecniche classiche di identificazione (ovvero i Metodi a Predizione d'Errore e gli algoritmi Subspace), questa tesi estende ed analizza alcuni aspetti chiave della procedura Bayesiana sopraccitata. In particolare, la tesi si sviluppa su quattro argomenti principali. 1. DESIGN DELLA DISTRIBUZIONE A PRIORI. Sfruttando la teoria delle distribuzioni a Massima Entropia, viene derivato un nuovo tipo di regolarizzazione l2 con l'obiettivo di penalizzare il rango della matrice di Hankel contenente i coefficienti di Markov. In tal modo è possibile controllare la complessità del modello stimato, misurata in termini del grado di McMillan. 2. CARATTERIZZAZIONE DELL'INCERTEZZA. Gli intervalli di confidenza costruiti dall'algoritmo di identificazione Bayesiana non-parametrica vengono analizzati e confrontati con quelli restituiti dai metodi parametrici a Predizione d'Errore. Convertendo quest'ultimi nelle loro approssimazioni campionarie, il confronto viene effettuato nello spazio a cui appartiene la risposta impulsiva. 3. STIMA ON-LINE. L'applicazione delle tecniche Bayesiane non-parametriche per l'identificazione dei sistemi viene estesa ad uno scenario on-line, in cui nuovi dati diventano disponibili ad intervalli di tempo prefissati. Vengono proposte due modifiche chiave della procedura standard off-line in modo da soddisfare i requisiti della stima real-time. Viene anche affrontata l'identificazione di sistemi tempo-varianti tramite l'introduzione, nel criterio di stima, di un fattore di dimenticanza, il quale e' in seguito trattato come un iper-parametro. 4. RIDUZIONE DEL MODELLO STIMATO. Le tecniche di identificazione Bayesiana non-parametrica restituiscono una stima della risposta impulsiva del sistema sconosciuto, ovvero un modello con un alto (verosimilmente infinito) grado di McMillan. Viene quindi proposta un'apposita procedura per ridurre tale modello ad un grado più basso, in modo che risulti più adatto per future applicazioni di controllo e filtraggio. Vengono inoltre confrontati diversi criteri per la selezione dell'ordine del modello ridotto.
5
Angola, Enrique. "Novelty Detection Of Machinery Using A Non-Parametric Machine Learning Approach." ScholarWorks @ UVM, 2018. https://scholarworks.uvm.edu/graddis/923.
Full textAbstract:
A novelty detection algorithm inspired by human audio pattern recognition is conceptualized and experimentally tested. This anomaly detection technique can be used to monitor the health of a machine or could also be coupled with a current state of the art system to enhance its fault detection capabilities. Time-domain data obtained from a microphone is processed by applying a short-time FFT, which returns time-frequency patterns. Such patterns are fed to a machine learning algorithm, which is designed to detect novel signals and identify windows in the frequency domain where such novelties occur. The algorithm presented in this paper uses one-dimensional kernel density estimation for different frequency bins. This process eliminates the need for data dimension reduction algorithms. The method of "pseudo-likelihood cross validation" is used to find an independent optimal kernel bandwidth for each frequency bin. Metrics such as the "Individual Node Relative Difference" and "Total Novelty Score" are presented in this work, and used to assess the degree of novelty of a new signal. Experimental datasets containing synthetic and real novelties are used to illustrate and test the novelty detection algorithm. Novelties are successfully detected in all experiments. The presented novelty detection technique could greatly enhance the performance of current state-of-the art condition monitoring systems, or could also be used as a stand-alone system.
6
Bartcus, Marius. "Bayesian non-parametric parsimonious mixtures for model-based clustering." Thesis, Toulon, 2015. http://www.theses.fr/2015TOUL0010/document.
Full textAbstract:
Cette thèse porte sur l’apprentissage statistique et l’analyse de données multi-dimensionnelles. Elle se focalise particulièrement sur l’apprentissage non supervisé de modèles génératifs pour la classification automatique. Nous étudions les modèles de mélanges Gaussians, aussi bien dans le contexte d’estimation par maximum de vraisemblance via l’algorithme EM, que dans le contexte Bayésien d’estimation par Maximum A Posteriori via des techniques d’échantillonnage par Monte Carlo. Nous considérons principalement les modèles de mélange parcimonieux qui reposent sur une décomposition spectrale de la matrice de covariance et qui offre un cadre flexible notamment pour les problèmes de classification en grande dimension. Ensuite, nous investiguons les mélanges Bayésiens non-paramétriques qui se basent sur des processus généraux flexibles comme le processus de Dirichlet et le Processus du Restaurant Chinois. Cette formulation non-paramétrique des modèles est pertinente aussi bien pour l’apprentissage du modèle, que pour la question difficile du choix de modèle. Nous proposons de nouveaux modèles de mélanges Bayésiens non-paramétriques parcimonieux et dérivons une technique d’échantillonnage par Monte Carlo dans laquelle le modèle de mélange et son nombre de composantes sont appris simultanément à partir des données. La sélection de la structure du modèle est effectuée en utilisant le facteur de Bayes. Ces modèles, par leur formulation non-paramétrique et parcimonieuse, sont utiles pour les problèmes d’analyse de masses de données lorsque le nombre de classe est indéterminé et augmente avec les données, et lorsque la dimension est grande. Les modèles proposés validés sur des données simulées et des jeux de données réelles standard. Ensuite, ils sont appliqués sur un problème réel difficile de structuration automatique de données bioacoustiques complexes issues de signaux de chant de baleine. Enfin, nous ouvrons des perspectives Markoviennes via les processus de Dirichlet hiérarchiques pour les modèles Markov cachésThis thesis focuses on statistical learning and multi-dimensional data analysis. It particularly focuses on unsupervised learning of generative models for model-based clustering. We study the Gaussians mixture models, in the context of maximum likelihood estimation via the EM algorithm, as well as in the Bayesian estimation context by maximum a posteriori via Markov Chain Monte Carlo (MCMC) sampling techniques. We mainly consider the parsimonious mixture models which are based on a spectral decomposition of the covariance matrix and provide a flexible framework particularly for the analysis of high-dimensional data. Then, we investigate non-parametric Bayesian mixtures which are based on general flexible processes such as the Dirichlet process and the Chinese Restaurant Process. This non-parametric model formulation is relevant for both learning the model, as well for dealing with the issue of model selection. We propose new Bayesian non-parametric parsimonious mixtures and derive a MCMC sampling technique where the mixture model and the number of mixture components are simultaneously learned from the data. The selection of the model structure is performed by using Bayes Factors. These models, by their non-parametric and sparse formulation, are useful for the analysis of large data sets when the number of classes is undetermined and increases with the data, and when the dimension is high. The models are validated on simulated data and standard real data sets. Then, they are applied to a real difficult problem of automatic structuring of complex bioacoustic data issued from whale song signals. Finally, we open Markovian perspectives via hierarchical Dirichlet processes hidden Markov models
7
Mahler, Nicolas. "Machine learning methods for discrete multi-scale fows : application to finance." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2012. http://tel.archives-ouvertes.fr/tel-00749717.
Full textAbstract:
This research work studies the problem of identifying and predicting the trends of a single financial target variable in a multivariate setting. The machine learning point of view on this problem is presented in chapter I. The efficient market hypothesis, which stands in contradiction with the objective of trend prediction, is first recalled. The different schools of thought in market analysis, which disagree to some extent with the efficient market hypothesis, are reviewed as well. The tenets of the fundamental analysis, the technical analysis and the quantitative analysis are made explicit. We particularly focus on the use of machine learning techniques for computing predictions on time-series. The challenges of dealing with dependent and/or non-stationary features while avoiding the usual traps of overfitting and data snooping are emphasized. Extensions of the classical statistical learning framework, particularly transfer learning, are presented. The main contribution of this chapter is the introduction of a research methodology for developing trend predictive numerical models. It is based on an experimentation protocol, which is made of four interdependent modules. The first module, entitled Data Observation and Modeling Choices, is a preliminary module devoted to the statement of very general modeling choices, hypotheses and objectives. The second module, Database Construction, turns the target and explanatory variables into features and labels in order to train trend predictive numerical models. The purpose of the third module, entitled Model Construction, is the construction of trend predictive numerical models. The fourth and last module, entitled Backtesting and Numerical Results, evaluates the accuracy of the trend predictive numerical models over a "significant" test set via two generic backtesting plans. The first plan computes recognition rates of upward and downward trends. The second plan designs trading rules using predictions made over the test set. Each trading rule yields a profit and loss account (P&L), which is the cumulated earned money over time. These backtesting plans are additionally completed by interpretation functionalities, which help to analyze the decision mechanism of the numerical models. These functionalities can be measures of feature prediction ability and measures of model and prediction reliability. They decisively contribute to formulating better data hypotheses and enhancing the time-series representation, database and model construction procedures. This is made explicit in chapter IV. Numerical models, aiming at predicting the trends of the target variables introduced in chapter II, are indeed computed for the model construction methods described in chapter III and thoroughly backtested. The switch from one model construction approach to another is particularly motivated. The dramatic influence of the choice of parameters - at each step of the experimentation protocol - on the formulation of conclusion statements is also highlighted. The RNN procedure, which does not require any parameter tuning, has thus been used to reliably study the efficient market hypothesis. New research directions for designing trend predictive models are finally discussed.
8
GONÇALVES, JÚNIOR Paulo Mauricio. "Multivariate non-parametric statistical tests to reuse classifiers in recurring concept drifting environments." Universidade Federal de Pernambuco, 2013. https://repositorio.ufpe.br/handle/123456789/12226.
Full textAbstract:
Data streams are a recent processing model where data arrive continuously, in large quantities,
at high speeds, so that they must be processed on-line. Besides that, several private
and public institutions store large amounts of data that also must be processed. Traditional
batch classi ers are not well suited to handle huge amounts of data for basically
two reasons. First, they usually read the available data several times until convergence,
which is impractical in this scenario. Second, they imply that the context represented by
data is stable in time, which may not be true. In fact, the context change is a common
situation in data streams, and is named concept drift.
This thesis presents rcd, a framework that o ers an alternative approach to handle
data streams that su er from recurring concept drifts. It creates a new classi er to each
context found and stores a sample of the data used to build it. When a new concept drift
occurs, rcd compares the new context to old ones using a non-parametric multivariate
statistical test to verify if both contexts come from the same distribution. If so, the
corresponding classi er is reused. If not, a new classi er is generated and stored.
Three kinds of tests were performed. One compares the rcd framework with several
adaptive algorithms (among single and ensemble approaches) in arti cial and real data
sets, among the most used in the concept drift research area, with abrupt and gradual
concept drifts. It is observed the ability of the classi ers in representing each context,
how they handle concept drift, and training and testing times needed to evaluate the
data sets. Results indicate that rcd had similar or better statistical results compared to
the other classi ers. In the real-world data sets, rcd presented accuracies close to the
best classi er in each data set.
Another test compares two statistical tests (knn and Cramer) in their capability in
representing and identifying contexts. Tests were performed using adaptive and batch
classi ers as base learners of rcd, in arti cial and real-world data sets, with several
rates-of-change. Results indicate that, in average, knn had better results compared to
the Cramer test, and was also faster. Independently of the test used, rcd had higher
accuracy values compared to their respective base learners.
It is also presented an improvement in the rcd framework where the statistical tests are performed in parallel through the use of a thread pool. Tests were performed in
three processors with di erent numbers of cores. Better results were obtained when there
was a high number of detected concept drifts, the bu er size used to represent each
data distribution was large, and there was a high test frequency. Even if none of these
conditions apply, parallel and sequential execution still have very similar performances.
Finally, a comparison between six di erent drift detection methods was also performed,
comparing the predictive accuracies, evaluation times, and drift handling, including
false alarm and miss detection rates, as well as the average distance to the drift
point and its standard deviation.Submitted by João Arthur Martins (joao.arthur@ufpe.br) on 2015-03-12T18:02:08Z No. of bitstreams: 2 Tese Paulo Gonçalves Jr..pdf: 2957463 bytes, checksum: de163caadf10cbd5442e145778865224 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5)
Made available in DSpace on 2015-03-12T18:02:08Z (GMT). No. of bitstreams: 2 Tese Paulo Gonçalves Jr..pdf: 2957463 bytes, checksum: de163caadf10cbd5442e145778865224 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013-04-23
Fluxos de dados s~ao um modelo de processamento de dados recente, onde os dados chegam continuamente, em grandes quantidades, a altas velocidades, de modo que eles devem ser processados em tempo real. Al em disso, v arias institui c~oes p ublicas e privadas armazenam grandes quantidades de dados que tamb em devem ser processadas. Classi cadores tradicionais n~ao s~ao adequados para lidar com grandes quantidades de dados por basicamente duas raz~oes. Primeiro, eles costumam ler os dados dispon veis v arias vezes at e convergirem, o que e impratic avel neste cen ario. Em segundo lugar, eles assumem que o contexto representado por dados e est avel no tempo, o que pode n~ao ser verdadeiro. Na verdade, a mudan ca de contexto e uma situa c~ao comum em uxos de dados, e e chamado de mudan ca de conceito. Esta tese apresenta o rcd, uma estrutura que oferece uma abordagem alternativa para lidar com os uxos de dados que sofrem de mudan cas de conceito recorrentes. Ele cria um novo classi cador para cada contexto encontrado e armazena uma amostra dos dados usados para constru -lo. Quando uma nova mudan ca de conceito ocorre, rcd compara o novo contexto com os antigos, utilizando um teste estat stico n~ao param etrico multivariado para veri car se ambos os contextos prov^em da mesma distribui c~ao. Se assim for, o classi cador correspondente e reutilizado. Se n~ao, um novo classi cador e gerado e armazenado. Tr^es tipos de testes foram realizados. Um compara o rcd com v arios algoritmos adaptativos (entre as abordagens individuais e de agrupamento) em conjuntos de dados arti ciais e reais, entre os mais utilizados na area de pesquisa de mudan ca de conceito, com mudan cas bruscas e graduais. E observada a capacidade dos classi cadores em representar cada contexto, como eles lidam com as mudan cas de conceito e os tempos de treinamento e teste necess arios para avaliar os conjuntos de dados. Os resultados indicam que rcd teve resultados estat sticos semelhantes ou melhores, em compara c~ao com os outros classi cadores. Nos conjuntos de dados do mundo real, rcd apresentou precis~oes pr oximas do melhor classi cador em cada conjunto de dados. Outro teste compara dois testes estat sticos (knn e Cramer) em suas capacidades de representar e identi car contextos. Os testes foram realizados utilizando classi cadores xi xii RESUMO tradicionais e adaptativos como base do rcd, em conjuntos de dados arti ciais e do mundo real, com v arias taxas de varia c~ao. Os resultados indicam que, em m edia, KNN obteve melhores resultados em compara c~ao com o teste de Cramer, al em de ser mais r apido. Independentemente do crit erio utilizado, rcd apresentou valores mais elevados de precis~ao em compara c~ao com seus respectivos classi cadores base. Tamb em e apresentada uma melhoria do rcd onde os testes estat sticos s~ao executadas em paralelo por meio do uso de um pool de threads. Os testes foram realizados em tr^es processadores com diferentes n umeros de n ucleos. Melhores resultados foram obtidos quando houve um elevado n umero de mudan cas de conceito detectadas, o tamanho das amostras utilizadas para representar cada distribui c~ao de dados era grande, e havia uma alta freq u^encia de testes. Mesmo que nenhuma destas condi c~oes se aplicam, a execu c~ao paralela e seq uencial ainda t^em performances muito semelhantes. Finalmente, uma compara c~ao entre seis diferentes m etodos de detec c~ao de mudan ca de conceito tamb em foi realizada, comparando a precis~ao, os tempos de avalia c~ao, manipula c~ao das mudan cas de conceito, incluindo as taxas de falsos positivos e negativos, bem como a m edia da dist^ancia ao ponto de mudan ca e o seu desvio padr~ao.
9
Gonçalves, Júnior Paulo Mauricio. "Multivariate non-parametric statistical tests to reuse classifiers in recurring concept drifting environments." Universidade Federal de Pernambuco, 2013. https://repositorio.ufpe.br/handle/123456789/12288.
Full textAbstract:
Data streams are a recent processing model where data arrive continuously, in large quantities,
at high speeds, so that they must be processed on-line. Besides that, several private
and public institutions store large amounts of data that also must be processed. Traditional
batch classi ers are not well suited to handle huge amounts of data for basically
two reasons. First, they usually read the available data several times until convergence,
which is impractical in this scenario. Second, they imply that the context represented by
data is stable in time, which may not be true. In fact, the context change is a common
situation in data streams, and is named concept drift.
This thesis presents rcd, a framework that o ers an alternative approach to handle
data streams that su er from recurring concept drifts. It creates a new classi er to each
context found and stores a sample of the data used to build it. When a new concept drift
occurs, rcd compares the new context to old ones using a non-parametric multivariate
statistical test to verify if both contexts come from the same distribution. If so, the
corresponding classi er is reused. If not, a new classi er is generated and stored.
Three kinds of tests were performed. One compares the rcd framework with several
adaptive algorithms (among single and ensemble approaches) in arti cial and real data
sets, among the most used in the concept drift research area, with abrupt and gradual
concept drifts. It is observed the ability of the classi ers in representing each context,
how they handle concept drift, and training and testing times needed to evaluate the
data sets. Results indicate that rcd had similar or better statistical results compared to
the other classi ers. In the real-world data sets, rcd presented accuracies close to the
best classi er in each data set.
Another test compares two statistical tests (knn and Cramer) in their capability in
representing and identifying contexts. Tests were performed using adaptive and batch
classi ers as base learners of rcd, in arti cial and real-world data sets, with several
rates-of-change. Results indicate that, in average, knn had better results compared to
the Cramer test, and was also faster. Independently of the test used, rcd had higher
accuracy values compared to their respective base learners.
It is also presented an improvement in the rcd framework where the statistical tests are performed in parallel through the use of a thread pool. Tests were performed in
three processors with di erent numbers of cores. Better results were obtained when there
was a high number of detected concept drifts, the bu er size used to represent each
data distribution was large, and there was a high test frequency. Even if none of these
conditions apply, parallel and sequential execution still have very similar performances.
Finally, a comparison between six di erent drift detection methods was also performed,
comparing the predictive accuracies, evaluation times, and drift handling, including
false alarm and miss detection rates, as well as the average distance to the drift
point and its standard deviation.Submitted by João Arthur Martins (joao.arthur@ufpe.br) on 2015-03-12T19:25:11Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) tese Paulo Mauricio Gonçalves Jr..pdf: 2957463 bytes, checksum: de163caadf10cbd5442e145778865224 (MD5)
Made available in DSpace on 2015-03-12T19:25:11Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) tese Paulo Mauricio Gonçalves Jr..pdf: 2957463 bytes, checksum: de163caadf10cbd5442e145778865224 (MD5) Previous issue date: 2013-04-23
Fluxos de dados s~ao um modelo de processamento de dados recente, onde os dados chegam continuamente, em grandes quantidades, a altas velocidades, de modo que eles devem ser processados em tempo real. Al em disso, v arias institui c~oes p ublicas e privadas armazenam grandes quantidades de dados que tamb em devem ser processadas. Classi cadores tradicionais n~ao s~ao adequados para lidar com grandes quantidades de dados por basicamente duas raz~oes. Primeiro, eles costumam ler os dados dispon veis v arias vezes at e convergirem, o que e impratic avel neste cen ario. Em segundo lugar, eles assumem que o contexto representado por dados e est avel no tempo, o que pode n~ao ser verdadeiro. Na verdade, a mudan ca de contexto e uma situa c~ao comum em uxos de dados, e e chamado de mudan ca de conceito. Esta tese apresenta o rcd, uma estrutura que oferece uma abordagem alternativa para lidar com os uxos de dados que sofrem de mudan cas de conceito recorrentes. Ele cria um novo classi cador para cada contexto encontrado e armazena uma amostra dos dados usados para constru -lo. Quando uma nova mudan ca de conceito ocorre, rcd compara o novo contexto com os antigos, utilizando um teste estat stico n~ao param etrico multivariado para veri car se ambos os contextos prov^em da mesma distribui c~ao. Se assim for, o classi cador correspondente e reutilizado. Se n~ao, um novo classi cador e gerado e armazenado. Tr^es tipos de testes foram realizados. Um compara o rcd com v arios algoritmos adaptativos (entre as abordagens individuais e de agrupamento) em conjuntos de dados arti ciais e reais, entre os mais utilizados na area de pesquisa de mudan ca de conceito, com mudan cas bruscas e graduais. E observada a capacidade dos classi cadores em representar cada contexto, como eles lidam com as mudan cas de conceito e os tempos de treinamento e teste necess arios para avaliar os conjuntos de dados. Os resultados indicam que rcd teve resultados estat sticos semelhantes ou melhores, em compara c~ao com os outros classi cadores. Nos conjuntos de dados do mundo real, rcd apresentou precis~oes pr oximas do melhor classi cador em cada conjunto de dados. Outro teste compara dois testes estat sticos (knn e Cramer) em suas capacidades de representar e identi car contextos. Os testes foram realizados utilizando classi cadores tradicionais e adaptativos como base do rcd, em conjuntos de dados arti ciais e do mundo real, com v arias taxas de varia c~ao. Os resultados indicam que, em m edia, KNN obteve melhores resultados em compara c~ao com o teste de Cramer, al em de ser mais r apido. Independentemente do crit erio utilizado, rcd apresentou valores mais elevados de precis~ao em compara c~ao com seus respectivos classi cadores base. Tamb em e apresentada uma melhoria do rcd onde os testes estat sticos s~ao executadas em paralelo por meio do uso de um pool de threads. Os testes foram realizados em tr^es processadores com diferentes n umeros de n ucleos. Melhores resultados foram obtidos quando houve um elevado n umero de mudan cas de conceito detectadas, o tamanho das amostras utilizadas para representar cada distribui c~ao de dados era grande, e havia uma alta freq u^encia de testes. Mesmo que nenhuma destas condi c~oes se aplicam, a execu c~ao paralela e seq uencial ainda t^em performances muito semelhantes. Finalmente, uma compara c~ao entre seis diferentes m etodos de detec c~ao de mudan ca de conceito tamb em foi realizada, comparando a precis~ao, os tempos de avalia c~ao, manipula c~ao das mudan cas de conceito, incluindo as taxas de falsos positivos e negativos, bem como a m edia da dist^ancia ao ponto de mudan ca e o seu desvio padr~ao.
10
Wei, Wei. "Probabilistic Models of Topics and Social Events." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/941.
Full textAbstract:
Structured probabilistic inference has shown to be useful in modeling complex latent structures of data. One successful way in which this technique has been applied is in the discovery of latent topical structures of text data, which is usually referred to as topic modeling. With the recent popularity of mobile devices and social networking, we can now easily acquire text data attached to meta information, such as geo-spatial coordinates and time stamps. This metadata can provide rich and accurate information that is helpful in answering many research questions related to spatial and temporal reasoning. However, such data must be treated differently from text data. For example, spatial data is usually organized in terms of a two dimensional region while temporal information can exhibit periodicities. While some work existing in the topic modeling community that utilizes some of the meta information, these models largely focused on incorporating metadata into text analysis, rather than providing models that make full use of the joint distribution of metainformation and text. In this thesis, I propose the event detection problem, which is a multidimensional latent clustering problem on spatial, temporal and topical data. I start with a simple parametric model to discover independent events using geo-tagged Twitter data. The model is then improved toward two directions. First, I augmented the model using Recurrent Chinese Restaurant Process (RCRP) to discover events that are dynamic in nature. Second, I studied a model that can detect events using data from multiple media sources. I studied the characteristics of different media in terms of reported event times and linguistic patterns. The approaches studied in this thesis are largely based on Bayesian nonparametric methods to deal with steaming data and unpredictable number of clusters. The research will not only serve the event detection problem itself but also shed light into a more general structured clustering problem in spatial, temporal and textual data.
Books on the topic "Non-parametric learning"
1
Carús, Pablo. Introdução às metodologias da investigação em motricidade humana. Manual prático de análises de dados com SPSS. Imprensa Universidade de Évora, 2020. http://dx.doi.org/10.24902/uevora.26.
Full textAbstract:
The Statistical Package for the Social Sciences (SPSS) is a computer statistical program used to perform various types of statistical analyses. This book aims to teach how to use SPSS, in an applied way in the introduction to research methodologies in human motricity. To do so, we start by teaching how to prepare a data file in SPSS and then by explaining how the transformation of new variables is carried out and the conditions of applicability of parametric tests. Finally, we attend to the learning procedure of computation and interpretation of the window of results of some parametric and non-parametric tests most used in human kinetics, for a basic-intermediate level.
Book chapters on the topic "Non-parametric learning"
1
Webb, Geoffrey I., Eamonn Keogh, Risto Miikkulainen, Risto Miikkulainen, and Michele Sebag. "Non-Parametric Methods." In Encyclopedia of Machine Learning, 722. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_598.
Full text
2
Krishnan, N. M. Anoop, Hariprasad Kodamana, and Ravinder Bhattoo. "Non-parametric Methods for Regression." In Machine Learning for Materials Discovery, 85–112. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-44622-1_5.
Full text
3
Szörényi, Balázs, Snir Cohen, and Shie Mannor. "Non-parametric Online AUC Maximization." In Machine Learning and Knowledge Discovery in Databases, 575–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71246-8_35.
Full text
4
Nguyen, Hoang-Vu, and Jilles Vreeken. "Non-parametric Jensen-Shannon Divergence." In Machine Learning and Knowledge Discovery in Databases, 173–89. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23525-7_11.
Full text
5
Hino, Hideitsu, and Noboru Murata. "A Non-parametric Maximum Entropy Clustering." In Artificial Neural Networks and Machine Learning – ICANN 2014, 113–20. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11179-7_15.
Full text
6
Koronakos, Gregory, and Dionisios N. Sotiropoulos. "Non-parametric Performance Measurement with Artificial Neural Networks." In Learning and Analytics in Intelligent Systems, 309–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49724-8_14.
Full text
7
Chen, Chun-Sheng, Christoph F. Eick, and Nouhad J. Rizk. "Mining Spatial Trajectories Using Non-parametric Density Functions." In Machine Learning and Data Mining in Pattern Recognition, 496–510. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23199-5_37.
Full text
8
Karlinsky, Leonid, and Shimon Ullman. "Using Linking Features in Learning Non-parametric Part Models." In Computer Vision – ECCV 2012, 326–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33712-3_24.
Full text
9
Rutkowski, Leszek, Maciej Jaworski, and Piotr Duda. "General Non-parametric Learning Procedure for Tracking Concept Drift." In Studies in Big Data, 155–72. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13962-9_9.
Full text
10
Babagholami-Mohamadabadi, Behnam, Seyed Mahdi Roostaiyan, Ali Zarghami, and Mahdieh Soleymani Baghshah. "Multi-Modal Distance Metric Learning: ABayesian Non-parametric Approach." In Computer Vision - ECCV 2014 Workshops, 63–77. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16199-0_5.
Full textConference papers on the topic "Non-parametric learning"
1
Hutchinson, Brian, and Jasha Droppo. "Learning non-parametric models of pronunciation." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947455.
Full text
2
Vaandrager, Maarten, Robert Babuska, Lucian Busoniu, and Gabriel A. D. Lopes. "Imitation learning with non-parametric regression." In 2012 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR 2012). IEEE, 2012. http://dx.doi.org/10.1109/aqtr.2012.6237681.
Full text
3
Silva-Lugo, Jose, Laura Warner, and Sebastian Galindo. "FROM PARAMETRIC TO NON-PARAMETRIC STATISTICS IN EDUCATION AND AGRICULTURAL EDUCATION RESEARCH." In 14th International Conference on Education and New Learning Technologies. IATED, 2022. http://dx.doi.org/10.21125/edulearn.2022.0841.
Full text
4
Kamath, Sudeep, Alon Orlitsky, Venkatadheeraj Pichapati, and Ehsan Zobeidi. "On Learning Parametric Non-Smooth Continuous Distributions." In 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2020. http://dx.doi.org/10.1109/isit44484.2020.9174474.
Full text
5
Baldwin, I., and P. Newman. "Non-parametric learning for natural plan generation." In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iros.2010.5651569.
Full text
6
Glaude, Hadrien, Fadi Akrimi, Matthieu Geist, and Olivier Pietquin. "A Non-parametric Approach to Approximate Dynamic Programming." In 2011 Tenth International Conference on Machine Learning and Applications (ICMLA). IEEE, 2011. http://dx.doi.org/10.1109/icmla.2011.19.
Full text
7
Wu, Zhirong, Yuanjun Xiong, Stella X. Yu, and Dahua Lin. "Unsupervised Feature Learning via Non-parametric Instance Discrimination." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00393.
Full text
8
Fu, Jiayi, Jinhong Zhong, Yunfeng Liu, Zhenyu Wang, and Ke Tang. "A non-parametric approach for learning from crowds." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727475.
Full text
9
Barlaud, Michel, and Frederic Guyard. "Learning a Sparse Generative Non-Parametric Supervised Autoencoder." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9414410.
Full text
10
Huang, Yanlong, Leonel Rozo, Joao Silverio, and Darwin G. Caldwell. "Non-parametric Imitation Learning of Robot Motor Skills." In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8794267.
Full textReports on the topic "Non-parametric learning"
1
Rodriguez, Fernando, and Guillermo Sapiro. Sparse Representations for Image Classification: Learning Discriminative and Reconstructive Non-Parametric Dictionaries. Fort Belvoir, VA: Defense Technical Information Center, June 2008. http://dx.doi.org/10.21236/ada513220.
Full text
2
Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Full textAbstract:
The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.