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335
A simple polynomialtime rescaling algorithm for solving linear programs
 In Proceedings of the 36th Annual ACM Symposium on Theory of Computing (STOC
, 2004
"... We show that the perceptron algorithm along with periodic rescaling solves linear programs in polynomial time. The algorithm requires no matrix inversions and no barrier functions. 1 ..."
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Cited by 50 (5 self)
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We show that the perceptron algorithm along with periodic rescaling solves linear programs in polynomial time. The algorithm requires no matrix inversions and no barrier functions. 1
DOI: 10.1007/S1133601091557 A KRAEMERTYPE RESCALING THAT TRANSFORMS THE ODDS RATIO INTO THE WEIGHTED KAPPA COEFFICIENT
, 2010
"... This paper presents a simple rescaling of the odds ratio that transforms the association measure into the weighted kappa statistic for a 2 × 2 table. Key words: Cohen’s kappa, 2 × 2 association measure. ..."
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This paper presents a simple rescaling of the odds ratio that transforms the association measure into the weighted kappa statistic for a 2 × 2 table. Key words: Cohen’s kappa, 2 × 2 association measure.
A deterministic rescaled perceptron algorithm
 Mathematical Programming
, 2013
"... Abstract The perceptron algorithm is a simple iterative procedure for finding a point in a convex cone F . At each iteration, the algorithm only involves a query to a separation oracle for F and a simple update on a trial solution. The perceptron algorithm is guaranteed to find a point in F after O ..."
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Abstract The perceptron algorithm is a simple iterative procedure for finding a point in a convex cone F . At each iteration, the algorithm only involves a query to a separation oracle for F and a simple update on a trial solution. The perceptron algorithm is guaranteed to find a point in F after
ABSTRACT A Simple Polynomialtime Rescaling Algorithm for Solving Linear Programs
"... The perceptron algorithm, developed mainly in the machine learning literature, is a simple greedy method for finding a feasible solution to a linear program (alternatively, for learning a threshold function.). In spite of its exponential worstcase complexity, it is often quite useful, in part due to ..."
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The perceptron algorithm, developed mainly in the machine learning literature, is a simple greedy method for finding a feasible solution to a linear program (alternatively, for learning a threshold function.). In spite of its exponential worstcase complexity, it is often quite useful, in part due
Fast Monte Carlo Algorithms for Matrices II: Computing a LowRank Approximation to a Matrix
 SIAM JOURNAL ON COMPUTING
, 2004
"... ... matrix A. It is often of interest to find a lowrank approximation to A, i.e., an approximation D to the matrix A of rank not greater than a specified rank k, where k is much smaller than m and n. Methods such as the Singular Value Decomposition (SVD) may be used to find an approximation to A ..."
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Cited by 216 (20 self)
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to A which is the best in a well defined sense. These methods require memory and time which are superlinear in m and n; for many applications in which the data sets are very large this is prohibitive. Two simple and intuitive algorithms are presented which, when given an m n matrix A, compute a
Chapter # VISUAL DATA MINING WITH SIMULTANEOUS RESCALING
"... Abstract: Visualization is used in data mining for the visual presentation of already discovered patterns and for discovering new patterns visually. Success in both tasks depends on the ability of presenting abstract patterns as simple visual patterns. Getting simple visualizations for complex abs ..."
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Abstract: Visualization is used in data mining for the visual presentation of already discovered patterns and for discovering new patterns visually. Success in both tasks depends on the ability of presenting abstract patterns as simple visual patterns. Getting simple visualizations for complex
The RaoWu Rescaling Bootstrap: From theory to practice
"... At Statistics Canada, variance estimation for complex surveys is mainly carried out using replication methods. The two replication methods which have been used in the last decade are the deleteone Primary Sampling Unit (PSU) jackknife and, more recently, the bootstrap. As Valliant (2007) rightly po ..."
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points out there are several variants of the bootstrap introduced by Efron (1979) in the i.i.d. case which are being used in survey sampling and we have to be clear which one is being referred to; at Statistics Canada we use solely the RaoWu rescaling bootstrap for production. Even though
A Rescaled Range Analysis of Random Events1
"... A b stract — The res c a l ed range st a t ist ical analysis was applied on sets of random numbers to demonstrate its potential in st u d y ing various types of biases and the presence of pe r i o d ical features. The data were generated by electro nic random number generators in psychokin es is t ..."
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, the variety of in fo rmation it can provide and its limitations are dis c u s s ed. The method prov i d es a re la t i vely simple, yet ro b u st, technique for st u d y ing ano m a l i es in random e ve n t s. K e y w o rd s: a no m a l i es — fractional Brownian motion — Hurst exponent — pe r i o d ic i
Chord recognition by fitting rescaled chroma vectors to chord templates
 Speech and Language Processing, 19(7):2222 – 2233
, 2011
"... Abstract—In this paper, we propose a simple and fast method for chord recognition in music signals. We extract a chromagram from the signal which transcribes the harmonic content of the piece over time. We introduce a set of chord templates taking into account one or more harmonics of the pitch note ..."
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Cited by 6 (0 self)
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Abstract—In this paper, we propose a simple and fast method for chord recognition in music signals. We extract a chromagram from the signal which transcribes the harmonic content of the piece over time. We introduce a set of chord templates taking into account one or more harmonics of the pitch
Applying the Multivariate TimeRescaling Theorem to Neural Population Models
, 2011
"... Statistical models of neural activity are integral to modern neuroscience. Recently interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However, any s ..."
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Cited by 2 (1 self)
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statistical model must be validated by an appropriate goodnessoffit test. KolmogorovSmirnov tests based on the timerescaling theorem have proven to be useful for evaluating pointprocessbased statistical models of singleneuron spike trains. Here we discuss the extension of the timerescaling theorem
Results 1  10
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