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129
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 599 (51 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.
A Survey of Robot Learning from Demonstration
"... We present a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a ..."
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Cited by 281 (19 self)
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We present a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a structure in which to categorize LfD research. Specifically, we analyze and categorize the multiple ways in which examples are gathered, ranging from teleoperation to imitation, as well as the various techniques for policy derivation, including matching functions, dynamics models and plans. To conclude we discuss LfD limitations and related promising areas for future research.
Constructive Incremental Learning From Only Local Information
 NEURAL COMPUTATION
"... We introduce a constructive, incremental learning system for regression problems that models data by means of spatially localized linear models. In contrast to other approaches, the size and shape of the receptive field of each locally linear model as well as the parameters of the locally linear mod ..."
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Cited by 208 (40 self)
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We introduce a constructive, incremental learning system for regression problems that models data by means of spatially localized linear models. In contrast to other approaches, the size and shape of the receptive field of each locally linear model as well as the parameters of the locally linear model itself are learned independently, i.e., without the need for competition or any other kind of communication. Independent learning is accomplished by incrementally minimizing a weighted local cross validation error. As a result, we obtain a learning system that can allocate resources as needed while dealing with the biasvariance dilemma in a principled way. The spatial localization of the linear models increases robustness towards negative interference. Our learning system can be interpreted as a nonparametric adaptive bandwidth smoother, as a mixture of experts where the experts are trained in isolation, and as a learning system which profits from combining independent expert knowledge on the same problem. This paper illustrates the potential learning capabilities of purely local learning and offers an interesting and powerful approach to learning with receptive fields.
Statistical Issues in cDNA Microarray Data Analysis
, 2003
"... This article summarizes some of the issues involved and provides a brief review of the analysis tools which are available to researchers to deal with them. Any microarray experiment involves a number of distinct stages. Firstly there is the design of the experiment. The researchers must decide which ..."
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Cited by 83 (6 self)
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This article summarizes some of the issues involved and provides a brief review of the analysis tools which are available to researchers to deal with them. Any microarray experiment involves a number of distinct stages. Firstly there is the design of the experiment. The researchers must decide which genes are to be printed on the arrays, which sources of RNA are to be hybridized to the arrays and on how many arrays the hybridizations will be replicated. Secondly, after hybridization, there follows a number of datacleaning steps or `lowlevel analysis' of the microarray data. The microarray images must be processed to acquire red and green foreground and background intensities for each spot. The acquired red/green ratios must be normalized to adjust for dyebias and for any systematic variation other than that due to the differences between the RNA samples being studied. Thirdly, the normalized ratios are analyzed by various graphical and numerical means to select differentially expressed genes or to find groups of genes whose expression profiles can reliably classify the different RNA sources into meaningful groups. The sections of this article correspond roughly to the various analysis steps. The following notation will be used throughout the article. The foreground red and green intensities will be written Pp and 9p for each spot. The background intensities will be Pf and 9f . The backgroundcorrected intensities will be P and 9 where usually P Pp Pf 0 # and 9 9p 9f 0 # . The logdifferential expression ratio will be vyq # E P 9 0 for each spot. Finally, the logintensity of the spot will be vyq 3 P9 0 , a measure of the overall brightness of the spot. (The letter E is a mnemonic for minus as vyq vyq E P 9 0 # while 3 is a mnemonic for add as #vyq vyq #...
Normalization and analysis of DNA microarray data by selfconsistency and local regression
"... With the advent of DNA hybridization microarrays comes the remarkable ability, in principle, to simultaneously monitor the expression levels of large numbers of genes. The quantitative comparison of 2 or more microarrays can reveal, for example, the distinct patterns of gene expression that dene die ..."
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Cited by 74 (0 self)
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With the advent of DNA hybridization microarrays comes the remarkable ability, in principle, to simultaneously monitor the expression levels of large numbers of genes. The quantitative comparison of 2 or more microarrays can reveal, for example, the distinct patterns of gene expression that dene dierent cellular phenotypes or the genes induced in the cellular response to insult or changing environmental conditions. Normalization of the measured intensities is a prerequisite of such comparisons, and indeed of any statistical analysis, yet little attention has been paid to its systematic study. The most straightforward normalization techniques in use rest on the implicit assumption of linear response between true expression level and output intensity. We nd that these assumptions are not generally met and that these simple methods can be improved. We have developed a robust semiparametric normalization technique based upon the assumption that the large majority of genes will not have...
Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers
"... The rapid growth in demand for computational power driven by modern service applications combined with the shift to the Cloud computing model have led to the establishment of largescale virtualized data centers. Such data centers consume enormous amounts of electrical energy resulting in high opera ..."
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Cited by 51 (5 self)
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The rapid growth in demand for computational power driven by modern service applications combined with the shift to the Cloud computing model have led to the establishment of largescale virtualized data centers. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. Dynamic consolidation of virtual machines (VMs) using live migration and switching idle nodes to the sleep mode allow Cloud providers to optimize resource usage and reduce energy consumption. However, the obligation of providing high quality of service to customers leads to the necessity in dealing with the energyperformance tradeoff, as aggressive consolidation may lead to performance degradation. Due to the variability of workloads experienced by modern applications, the VM placement should be optimized continuously in an online manner. To understand the implications of the online nature of the problem, we conduct competitive analysis and prove competitive ratios of optimal online deterministic algorithms for the single VM migration and dynamic VM consolidation problems. Furthermore, we propose novel adaptive heuristics for dynamic consolidation of VMs based on an analysis of historical data from the resource usage by VMs. The proposed algorithms significantly reduce energy consumption, while ensuring a high level of adherence to the Service Level Agreements (SLA). We validate the high efficiency of the proposed algorithms by extensive simulations using realworld workload traces from more than a thousand
Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning
, 2000
"... Locally weighted learning (LWL) is a class of techniques from nonparametric statistics that provides useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of robotic systems. This paper introduces several LWL algorithms that have been ..."
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Cited by 50 (3 self)
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Locally weighted learning (LWL) is a class of techniques from nonparametric statistics that provides useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of robotic systems. This paper introduces several LWL algorithms that have been tested successfully in realtime learning of complex robot tasks. We discuss two major classes of LWL, memorybased LWL and purely incremental LWL that does not need to remember any data explicitly. In contrast to the traditional belief that LWL methods cannot work well in highdimensional spaces, we provide new algorithms that have been tested on up to 90 dimensional learning problems. The applicability of our LWL algorithms is demonstrated in various robot learning examples, including the learning of devilsticking, polebalancing by a humanoid robot arm, and inversedynamics learning for a seven and a 30 degreeoffreedom robot. In all these examples, the application of our statistical n...
A New Method for Varying Adaptive Bandwidth Selection
 IEEE Trans. on Signal Proc
, 1999
"... A novel approach is developed to solve a problem of varying bandwidth selection for filtering a signal given with an additive noise. The approach is based on the intersection of confidence intervals (ICI) rule and gives the algorithm, which is simple to implement and adaptive to unknown smoothness o ..."
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Cited by 46 (24 self)
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A novel approach is developed to solve a problem of varying bandwidth selection for filtering a signal given with an additive noise. The approach is based on the intersection of confidence intervals (ICI) rule and gives the algorithm, which is simple to implement and adaptive to unknown smoothness of the signal.
RealTime Robot Learning With Locally Weighted Statistical Learning
, 2000
"... Locally weighted learning (LWL) is a class of statistical learning techniques that provides useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of robotic systems. This paper introduces several LWL algorithms that have been tested s ..."
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Cited by 43 (8 self)
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Locally weighted learning (LWL) is a class of statistical learning techniques that provides useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of robotic systems. This paper introduces several LWL algorithms that have been tested successfully in realtime learning of complex robot tasks. We discuss two major classes of LWL, memorybased LWL and purely incremental LWL that does not need to remember any data explicitly. In contrast to the traditional belief that LWL methods cannot work well in highdimensional spaces, we provide new algorithms that have been tested on up to 50 dimensional learning problems. The applicability of our LWL algorithms is demonstrated in various robot learning examples, including the learning of devilsticking, polebalancing by a humanoid robot arm, and inversedynamics learning for a seven degreeoffreedom robot.