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63
Mining of concurrent text and time series
 In proceedings of the 6 th ACM SIGKDD Int'l Conference on Knowledge Discovery and Data Mining Workshop on Text Mining
, 2000
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Markov random fields in statistics
 Disorder in Physical Systems. A Volume in Honour of John M. Hammersley
, 1990
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Overfitting Explained
, 1997
"... Overfitting arises when model components are evaluated against the wrong reference distribution. Most modeling algorithms iteratively find the best of several components and then test whether this component is good enough to add to the model. We show that for independently distributed random variabl ..."
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Cited by 24 (2 self)
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Overfitting arises when model components are evaluated against the wrong reference distribution. Most modeling algorithms iteratively find the best of several components and then test whether this component is good enough to add to the model. We show that for independently distributed random variables, the reference distribution for any one variable underestimates the reference distribution for the the highestvalued variable # thus variate values will appear significant when they are not, and model components will be added when they should not be added. We relate this problem to the wellknown statistical theory of multiple comparisons or simultaneous inference.
Wavelet Analysis of Covariance with Application to Atmospheric Time Series
 J. Geophys. Res
, 2000
"... Introduction The bivariate relationship between two time series is often of crucial interest in atmospheric science. For example, the MaddenJulian oscillation (MJO) [3] was found using bivariate spectral analysis between the station pressure and zonal wind components at Canton Island  specifica ..."
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Cited by 20 (2 self)
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Introduction The bivariate relationship between two time series is often of crucial interest in atmospheric science. For example, the MaddenJulian oscillation (MJO) [3] was found using bivariate spectral analysis between the station pressure and zonal wind components at Canton Island  specifically the cospectrum and magnitude squared coherence. We introduce the wavelet covariance and correlation between two time series based upon the maximal overlap DWT [6] and Daubechies families of wavelets [1, Sec. 6.2]. Key points include 4 the wavelet covariance decomposes the usual covariance on a scale by scale basis, 4 approximate confidence intervals may be calculated for estimators of the wavelet covariance and correlation, and 4 the wavelet crosscovariance and crosscorrelation are used to investigate lead/lag relationships. We apply these wavelet estimators to the bivariate analysis of the Southern Oscillation Index (SOI), an
Adjusting for multiple comparisons in decision tree pruning
 Proc. 3rd Int. Conf. on Knowledge Discovery & Data Mining (KDD97
, 1997
"... Pruning is a common technique to avoid over tting in decision trees. Most pruning techniques do not account for one important factor  multiple comparisons. Multiple comparisons occur when an induction algorithm examines several candidate models and selects the one that best accords with the data. M ..."
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Cited by 17 (4 self)
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Pruning is a common technique to avoid over tting in decision trees. Most pruning techniques do not account for one important factor  multiple comparisons. Multiple comparisons occur when an induction algorithm examines several candidate models and selects the one that best accords with the data. Making multiple comparisons produces incorrect inferences about model accuracy. We examine a method that adjusts for multiple comparisons when pruning decision trees { Bonferroni pruning. In experiments with arti cial and realistic datasets, Bonferroni pruning produces smaller trees that are at least as accurate as trees pruned using other common approaches.
Tensor diagonalization, a useful tool in signal processing
 IFAC SYMPOSIUM ON SYSTEM IDENTIFICATION
, 1994
"... Tensors appear more and more often in signal processing problems, and especially spatial processing, which typically involves multichannel modeling. Even if it is not always obvious that tensor algebra is the best framework to address a problem, there are cases where no choice is left. Blind identif ..."
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Cited by 17 (5 self)
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Tensors appear more and more often in signal processing problems, and especially spatial processing, which typically involves multichannel modeling. Even if it is not always obvious that tensor algebra is the best framework to address a problem, there are cases where no choice is left. Blind identification of multichannel non monic MA models is given as an illustrating example of this claim.
Web Proxy Workload Characterisation And Modelling
, 1999
"... Understanding WWW traffic characteristics is key to improving the performance and scalability of the Web. In the first part of this thesis, Web proxy workloads from different levels of a caching hierarchy are used to understand how the workload characteristics change across different levels of a cac ..."
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Cited by 11 (2 self)
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Understanding WWW traffic characteristics is key to improving the performance and scalability of the Web. In the first part of this thesis, Web proxy workloads from different levels of a caching hierarchy are used to understand how the workload characteristics change across different levels of a caching hierarchy. The main observations of this study are: HTML and image documents account for 95% of the documents seen in the workload; the distribution of transfer sizes of documents is heavytailed, with the tails becoming heavier as one moves from the client side to the server side of the network; the popularity profile of documents does not precisely follow the Zipf distribution; onetimers account for approximately 70% of the documents referenced; concentration of references is less at proxy caches than at servers, and concentration of references is higher at lowerlevel proxies than at higherlevel proxies; there appears to be no correlation between document modification rate and document pop...
Learning What Is Relevant to the Effects of Actions for a Mobile Robot
, 1998
"... We have developed a learning mechanism that allows robots to discover the conditional effects of their actions. Based on sensorimotor experience, this mechanism permits a robot to explore its environment and observe effects of its actions. These observations are used to learn a context operator diff ..."
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Cited by 10 (4 self)
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We have developed a learning mechanism that allows robots to discover the conditional effects of their actions. Based on sensorimotor experience, this mechanism permits a robot to explore its environment and observe effects of its actions. These observations are used to learn a context operator difference table, a structure that relates circumstances (context) and actions (operators) to effects on the environment. From the context operator difference table, one can extract a relatively small set of state variables, which simplifies the problem of learning policies for complex activities. We demonstrate results with the Pioneer 1 mobile robot.
Assessing Nonstationary Time Series Using Wavelets
, 1998
"... The discrete wavelet transform has be used extensively in the field of Statistics, mostly in the area of "denoising signals" or nonparametric regression. This thesis provides a new application for the discrete wavelet transform, assessing nonstationary events in time series  especially l ..."
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Cited by 10 (4 self)
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The discrete wavelet transform has be used extensively in the field of Statistics, mostly in the area of "denoising signals" or nonparametric regression. This thesis provides a new application for the discrete wavelet transform, assessing nonstationary events in time series  especially long memory processes. Long memory processes are those which exhibit substantial correlations between events separated by a long period of time. Departures from stationarity in these heavily autocorrelated time series, such as an abrupt change in the variance at an unknown location or "bursts" of increased variability, can be detected and accurately located using discrete wavelet transforms  both orthogonal and overcomplete. A cumulative sum of squares method, utilizing a KolomogorovSmirnovtype
change detection by statistical objectbased method. Remote Sensing of Environment, v
, 2006
"... Forest monitoring requires more automated systems to analyse the large amount of remote sensing data. A new method of change detection is proposed for identifying forest land cover change using high spatial resolution satellite images. Combining the advantages of image segmentation, image differenci ..."
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Cited by 8 (1 self)
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Forest monitoring requires more automated systems to analyse the large amount of remote sensing data. A new method of change detection is proposed for identifying forest land cover change using high spatial resolution satellite images. Combining the advantages of image segmentation, image differencing and stochastic analysis of the multispectral signal, this OBReflectance method is objectbased and statistically driven. From a multidate image, a single segmentation using regionmerging technique delineates multidate objects characterised by their reflectance differences statistics. Objects considered as outliers from multitemporal point of view are successfully discriminated thanks to a statistical procedure, i.e., the iterative trimming. Based on a chisquare test of hypothesis, abnormal values of reflectance differences statistics are identified and the corresponding objects are labelled as change. The objectbased method performances were assessed using two sources of reference data, including one independent forest inventory, and were compared to a pixelbased method using the RGBNDVI technique. High detection accuracy (>90%) and overall Kappa (>0.80) were achieved by OBReflectance method in temperate forests using three SPOTHRV images covering a 10year period.