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Boosting Density Function Estimators
[chapter]
2002
Lecture Notes in Computer Science
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In this paper, we focus on the adaptation of boosting to density function estimation, useful in a number of fields including Natural Language Processing and Computational Biology. ...
In this paper, we do not take into account classification errors to optimize a classifier, but rather density estimation errors to optimize an estimator (here a probabilistic automaton) of a given target ...
moment, i.e. the classic boosting, the boosting applied to protototype selection, and the boosting of density function estimation. ...
doi:10.1007/3-540-36755-1_36
fatcat:wnfgb3nbb5ffrjgu74wvvlt3mi
On Asymptotic Mean Integrated Squared Error's Reduction Techniques in Kernel Density Estimation
2019
International Journal of Computational and Theoretical Statistics
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The asymptotic mean integrated squared error (AMISE) is an optimality criterion function that measures the performance of a kernel density estimator. ...
The techniques of asymptotic mean integrated squared error's reduction in kernel density estimation is the focus of this paper. ...
Bandwidths and AMISE of Kernel Boosting Density
Estimate
m=1
m=2
Eruption length (minutes)
Density
Estimate Density
Estimate
Density
Estimate
Eruption length (minutes)
Eruption length ...
doi:10.12785/ijcts/060110
fatcat:cimuot76cfdc5gldvr3d3niz2y
A Meshsize Boosting Algorithm In Kernel Density Estimation
2008
Journal of Science and Technology (Ghana)
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This paper proposes a new algorithm for boosting in kernel density estimation (KDE). ...
Improved boosting algorithm using confidence rated prediction. Machine learn. 37: 297 -336. A meshsize boosting algorithm in kernel density estimation Ishiekwene et al. ...
INTRODUCTION Kernel density estimation (KDE) is a process of constructing a density function from a given set of data. ...
doi:10.4314/just.v28i2.33120
fatcat:ov2guojanrfxfaq7hrvkj5bc3i
Looking for lumps: boosting and bagging for density estimation
2002
Computational Statistics & Data Analysis
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Analogous to the boosting framework, the algorithms iteratively mix the current density estimator with an additional density chosen in a greedy fashion to optimize a fit criterion. ...
In this paper I extend these methods to the design of algorithms for density estimation for large, noisy, high dimensional datasets. ...
The author is grateful to Werner Stuetzle for discussions on boosting with respect to density estimation, Larry Wasserman for his suggestion to use EM to make the modification proposals, and Dan McCaffrey ...
doi:10.1016/s0167-9473(01)00066-4
fatcat:kaue3ze2ujdf7gxlymkbzk2ure
Learned-loss boosting
2012
Computational Statistics & Data Analysis
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This is done by jointly estimating a residual density for a prediction function at the same time as the prediction function is chosen to maximize the log likelihood of the estimated density. ...
as boosting continues. ...
Penalized Density Estimation The central technique in this paper is the estimation of a log density for the residuals of a prediction function. ...
doi:10.1016/j.csda.2012.05.019
fatcat:77hgjpjpynfd5gk7q6nfe4t7pi
Adaptive Kernel in Meshsize Boosting Algorithm in KDE
2011
African Research Review
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This paper proposes the use of adaptive kernel in a meshsize boosting algorithm in kernel density estimation. ...
This weight placed on the kernel estimator, is a ratio of a log function in which the denominator is a leave-one-out estimate of the density function. ...
Introduction Boosting in kernel density estimation was first proposed by Schapire (1990) . ...
doi:10.4314/afrrev.v5i1.64541
fatcat:nyerkrhv3vfqpdqv6fgynn7i4u
Boosting kernel density estimates: A bias reduction technique?
2004
Biometrika
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This paper proposes an algorithm for boosting kernel density estimates. ...
We show that boosting is closely linked to a previously proposed method of bias reduction and indicate how it should enjoy similar properties. ...
Instead of decision trees, Rather than the traditional goal of boosting classifiers, in this paper we consider the goal of density estimation, and investigate how kernel density estimates can be boosted ...
doi:10.1093/biomet/91.1.226
fatcat:xazjanyfsffcrfqootwwdkmkxe
Boosted coefficient models
2011
Statistics and computing
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Examples are given by conditional density estimation, hazard regression and regression with a functional response. ...
We propose to estimate these coefficient functions using boosted tree models. Algorithms are provided for the above three situations, and real data sets are used to investigate their performance. ...
covariates into a set of (univariate) real valued functions. For instance, in conditional density estimation the object is to estimate how the density of the response depends on the covariates. ...
doi:10.1007/s11222-011-9253-0
fatcat:yxqt4meecvhp3gwy4wyuweslzy
Page 4028 of Mathematical Reviews Vol. , Issue 2004e
[page]
2004
Mathematical Reviews
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This article provides nonparametric estimators that nearly equal the MLE estimates for the marginal densities while being close to the kernel nonparametric density estimates for the joint density estimates ...
estimates that incorporate marginal parametric density information. ...
Continuous Optimization based-on Boosting Gaussian Mixture Model
2006
18th International Conference on Pattern Recognition (ICPR'06)
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A new Estimation of Distribution Algorithm(EDA) based-on Gaussian Mixture Model (GMM) is proposed, in which boosting, an efficient ensemble learning method, is adopted to estimate GMM. ...
Moreover, since boosting can be viewed as a gradient search for a good fit of some objective in function space, the new EDA is time efficient. ...
Rosset et al. successfully applied the boosting methodology to the unsupervised learning problem of density estimation and proposed a general boosting density estimation framework [10] . ...
doi:10.1109/icpr.2006.412
dblp:conf/icpr/LinWZZ06
fatcat:2hyrvkloaffl5g3gc6nwh6w5rq
Prediction of Top Tourist Attraction Spots using Learning Algorithms
2019
International journal of recent technology and engineering
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For this purpose four algorithms such as Kernel Density Estimation, K- Nearest Neighbor, Random forest and XG Boost have been used. ...
The findings revealed that XG Boost yields better results in terms of accuracy than other three algorithms. ...
We used four machine learning algorithms such as KNN, KDE, Random forest, and XG Boost to train and test the classifiers for tourism-related opinion mining. ...
doi:10.35940/ijrte.c4241.098319
fatcat:cicp6vm25zd3tnecdw7cjz7z4q
Learning to Count with CNN Boosting
[chapter]
2016
Lecture Notes in Computer Science
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We follow modern learning approaches in which a density map is estimated directly from the input image. ...
We employ CNNs and incorporate two significant improvements to the state of the art methods: layered boosting and selective sampling. ...
Learning to Count with CNN Boosting ...
doi:10.1007/978-3-319-46475-6_41
fatcat:d74atjc4yjgbphwtf42vxwj4j4
LinCDE: Conditional Density Estimation via Lindsey's Method
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2107.12713v1 fatcat:vjcje2fntfgyxi5r6spmaz367q
In this paper, we propose a conditional density estimator based on gradient boosting and Lindsey's method (LinCDE). ...
In particular, when suitably parametrized, LinCDE will produce smooth and non-negative density estimates. Furthermore, like boosted regression trees, LinCDE does automatic feature selection. ...
LinCDE boosting can be used to estimate the density ratio provided with any nuisance baseline conditional density estimate. ...
arXiv:2107.12713v2
fatcat:wzacxdz6bjh4nmryd6ec6almze
Gradient Boosted Normalizing Flows
[article]
2020
arXiv
pre-print
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We demonstrate the effectiveness of this technique for density estimation and, by coupling GBNF with a variational autoencoder, generative modeling of images. ...
We propose an alternative: Gradient Boosted Normalizing Flows (GBNF) model a density by successively adding new NF components with gradient boosting. ...
Figure 4 : 4 Matching the energy functions from
Figure 5 : 5 Density estimation for 2D toy data. ...
arXiv:2002.11896v4
fatcat:tdq244q3bbeqzh2eq62ep6ukna
Boosting Independent Component Analysis
[article]
2021
arXiv
pre-print
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Our algorithm fills the gap in the nonparametric independent component analysis by introducing boosting to maximum likelihood estimation. ...
In this paper, we present a novel boosting-based algorithm for independent component analysis. ...
Estimating the source's density via boosting Boosting [34] , [35] is a technique of combining multiple weak learners to produce a powerful committee, whose performance is significantly better than any ...
arXiv:2112.06920v2
fatcat:tfqktff5hzfmbi75e7nmt6jvge
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