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Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 594 (53 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.
Using Vector Quantization for Image Processing
 Proc. IEEE
, 1993
"... Image compression is the process of reducing the number of bits required to represent an image. Vector quantization, the mapping of pixel intensity vectors into binary vectors indexing a limited number of possible reproductions, is a popular image compression algorithm. Compression has traditionally ..."
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Cited by 27 (2 self)
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Image compression is the process of reducing the number of bits required to represent an image. Vector quantization, the mapping of pixel intensity vectors into binary vectors indexing a limited number of possible reproductions, is a popular image compression algorithm. Compression has traditionally been done with little regard for image processing operations that may precede or follow the compression step. Recent work has used vector quantization both to simplify image processing tasks  such as enhancement, classification, halftoning, and edge detection  and to reduce the computational complexity by performing them simultaneously with the compression. After briefly reviewing the fundamental ideas of vector quantization, we present a survey of vector quantization algorithms that perform image processing. 1 Introduction Data compression is the mapping of a data set into a bit stream to decrease the number of bits required to represent the data set. With data compression, one can st...
Nonparametric prediction
 In Advances in Learning Theory: Methods, Models and Applications
"... ..."
Universally consistent latent position estimation and vertex classification for random dot product graphs
 IEEE Transactions on Pattern Analysis and Machine Intelligence (Accepted
, 2013
"... In this work we show that, using the eigendecomposition of the adjacency matrix, we can consistently estimate latent positions for random dot product graphs provided the latent positions are i.i.d. from some distribution. If class labels are observed for a number of vertices tending to infinity, th ..."
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Cited by 12 (9 self)
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In this work we show that, using the eigendecomposition of the adjacency matrix, we can consistently estimate latent positions for random dot product graphs provided the latent positions are i.i.d. from some distribution. If class labels are observed for a number of vertices tending to infinity, then we show that the remaining vertices can be classified with error converging to Bayes optimal using the knearestneighbors classification rule. We evaluate the proposed methods on simulated data and a graph derived from Wikipedia.
Information and Posterior Probability Criteria for Model Selection in Local Likelihood Estimation
 J Amer. Stat. Ass
, 1998
"... this paper we propose a modification to the methods used to motivate many information and posterior probability criteria for the weighted likelihood case. We derive weighted versions for two of the most widely known criteria, namely the AIC and BIC. Via a simple modification, the criteria are also m ..."
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Cited by 6 (0 self)
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this paper we propose a modification to the methods used to motivate many information and posterior probability criteria for the weighted likelihood case. We derive weighted versions for two of the most widely known criteria, namely the AIC and BIC. Via a simple modification, the criteria are also made useful for window span selection. The usefulness of the weighted version of these criteria are demonstrated through a simulation study and an application to three data sets. KEY WORDS: Information Criteria; Posterior Probability Criteria; Model Selection; Local Likelihood. 1. INTRODUCTION Local regression has become a popular method for smoothing scatterplots and for nonparametric regression in general. It has proven to be a useful tool in finding structure in datasets (Cleveland and Devlin 1988). Local regression estimation is a method for smoothing scatterplots (x i ; y i ), i = 1; : : : ; n in which the fitted value at x 0 is the value of a polynomial fit to the data using weighted least squares where the weight given to (x i ; y i ) is related to the distance between x i and x 0 . Stone (1977) shows that estimates obtained using the local regression methods have desirable theoretical properties. Recently, Fan (1993) has studied minimax properties of local linear regression. Tibshirani and Hastie (1987) extend the ideas of local regression to a local likelihood procedure. This procedure is designed for nonparametric regression modeling in situations where weighted least squares is inappropriate as an estimation method, for example binary data. Local regression may be viewed as a special case of local likelihood estimation. Tibshirani and Hastie (1987), Staniswalis (1989), and Loader (1999) apply local likelihood estimation to several types of data where local regressio...
ASYMPTOTIC PROPERTIES OF JUSTINTIME MODELS
"... The concept of JustinTime models has been introduced for models that are not estimated until they are really needed. The prediction is taken as a weighted average of neighboring points in the regressor space, such that an optimal bias/variance tradeoff is achieved. The asymptotic properties of t ..."
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Cited by 2 (2 self)
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The concept of JustinTime models has been introduced for models that are not estimated until they are really needed. The prediction is taken as a weighted average of neighboring points in the regressor space, such that an optimal bias/variance tradeoff is achieved. The asymptotic properties of the method are investigated, and are compared to the corresponding properties of related statistical nonparametric kernel methods. It is shown that the rate of convergence for JustinTime models at least is in the same order as traditional kernel estimators, and that better rates probably can be achieved.
LAZY LEARNING: A LOCAL METHOD FOR SUPERVISED LEARNING
"... The traditional approach to supervised learning is global modeling which describes the relationship between the input and the output with an analytical function over the whole input domain. What makes global modeling appealing is the nice property that even for huge datasets, a parametric model can ..."
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Cited by 1 (1 self)
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The traditional approach to supervised learning is global modeling which describes the relationship between the input and the output with an analytical function over the whole input domain. What makes global modeling appealing is the nice property that even for huge datasets, a parametric model can be stored in a small memory. Also, the evaluation of the parametric model requires a short program that can be executed in a reduced amount of time. Nevertheless, modeling complex input/output relations often requires the adoption of global nonlinear models, whose learning procedures are typically slow and analytically intractable. In particular, validation methods, which address the problem of assessing a global model on the basis of a finite amount of noisy samples, are computationally prohibitive. For these reasons, in recent years, interest has grown in pursuing
GMM Estimation of Panel Probit Models: Nonparametric Estimation of the Optimal Instruments
, 1995
"... Introduction For crosssection estimation of nonlinear models maximum likelihood (ML) is the typically used method. However, using ML on panel data is quite tough due to intertemporal correlations of the error terms. It is necessary to specify the correlation matrix and to evaluate Tvariate integr ..."
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Cited by 1 (1 self)
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Introduction For crosssection estimation of nonlinear models maximum likelihood (ML) is the typically used method. However, using ML on panel data is quite tough due to intertemporal correlations of the error terms. It is necessary to specify the correlation matrix and to evaluate Tvariate integrals which is a very computertimeconsuming task. One possibility is to use simulation methods in order to obtain approximations of the intergrals (Hajivassiliou, 1993), but these methods are still computerintensive. Butler and Moffitt (1982) propose a simplification by restricting the covariance to have a one factor random effects structure. Although they introduce an efficient algorithm for estimating ML, there is, to the best of our knowledge no proof available that the suggested estimator remains consistent when the true correlation structure has no one factor representation. Avery, Hansen and Hotz (1983) suggest to use only marginal moments of the observed dependent variable in e
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"... Rulebased and support vector (SV)regression/classification algorithms for joint processing of census, map, survey and district data by ..."
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Rulebased and support vector (SV)regression/classification algorithms for joint processing of census, map, survey and district data by
Controlling the estimation errors in the Finnish multisource National
, 2004
"... Katila, Matti. 2004. Controlling the estimation errors in the Finnish multisource National ..."
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Katila, Matti. 2004. Controlling the estimation errors in the Finnish multisource National