@conference {14024,
	title = {Approximate expressions for the mean and the covariance of the maximum likelihood estimator for acoustic source localization},
	booktitle = {Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP {\textquoteright}05). IEEE International Conference on},
	volume = {3},
	year = {2005},
	month = {2005/03//},
	pages = {iii/73 - iii/76 Vol. 3 - iii/73 - iii/76 Vol. 3},
	abstract = {Acoustic source localization using multiple microphones can be formulated as a maximum likelihood estimation problem. The estimator is implicitly defined as the minimum of a certain objective function. As a result, we cannot get explicit expressions for the mean and the covariance of the estimator. We derive approximate expressions for the mean vector and covariance matrix of the estimator using Taylor{\textquoteright}s series expansion of the implicitly defined estimator. The validity of our expressions is verified by Monte-Carlo simulations. We also study the performance of the estimator for different microphone array configurations.},
	keywords = {(mathematics);, acoustic, approximate, approximation, array, array;, covariance, estimation;, expansion;, expressions;, function;, likelihood, localization;, matrices;, matrix;, maximum, mean, microphone, objective, processing;, series, signal, source, Taylor, theory;, vector;, vectors;},
	doi = {10.1109/ICASSP.2005.1415649},
	author = {Raykar,V.C. and Duraiswami, Ramani}
}
@conference {12693,
	title = {Classification probability analysis of principal component space analysis},
	booktitle = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/08//},
	pages = {240 - 243 Vol.1 - 240 - 243 Vol.1},
	abstract = {In a previous paper, we have presented a new linear classification algorithm, principal component space analysis (PCNSA) which is designed for problems like object recognition where different classes have unequal and non-white noise covariance matrices. PCNSA first obtains a principal components space (PCA space) for the entire data and in this PCA space, it finds for each class "i", an M<sub>i</sub> dimensional subspace along which the class{\textquoteright}s intra-class variance is the smallest. We call this subspace an approximate space (ANS) since the lowest variance is usually "much smaller" than the highest. A query is classified into class "i" if its distance from the class{\textquoteright}s mean in the class{\textquoteright}s ANS is a minimum. In this paper, we discuss the PCNSA algorithm more precisely and derive tight upper bounds on its classification error probability. We use these expressions to compare classification performance of PCNSA with that of subspace linear discriminant analysis (SLDA).},
	keywords = {algorithm;, analysis;, approximate, classification, classification;, component, covariance, discriminant, error, intraclass, linear, matrices;, matrix;, noise;, nonwhite, object, pattern, PCA;, principal, probability;, recognition;, space, space;, statistics;, subspace, variance;},
	doi = {10.1109/ICPR.2004.1334068},
	author = {Vaswani, N. and Chellapa, Rama}
}
@conference {18038,
	title = {Efficient approximate and dynamic matching of patterns using a labeling paradigm},
	booktitle = {Foundations of Computer Science, 1996. Proceedings., 37th Annual Symposium on},
	year = {1996},
	month = {1996/10//},
	pages = {320 - 328},
	abstract = {A key approach in string processing algorithmics has been the labeling paradigm which is based on assigning labels to some of the substrings of a given string. If these labels are chosen consistently, they can enable fast comparisons of substrings. Until the first optimal parallel algorithm for suffix tree construction was given by the authors in 1994 the labeling paradigm was considered not to be competitive with other approaches. They show that this general method is also useful for several central problems in the area of string processing: approximate string matching, dynamic dictionary matching, and dynamic text indexing. The approximate string matching problem deals with finding all substrings of a text which match a pattern ldquo;approximately rdquo;, i.e., with at most m differences. The differences can be in the form of inserted, deleted, or replaced characters. The text indexing problem deals with finding all occurrences of a pattern in a text, after the text is preprocessed. In the dynamic text indexing problem, updates to the text in the form of insertions and deletions of substrings are permitted. The dictionary matching problem deals with finding all occurrences of each pattern set of a set of patterns in a text, after the pattern set is preprocessed. In the dynamic dictionary matching problem, insertions and deletions of patterns to the pattern set are permitted},
	keywords = {algorithm;replaced, algorithmics;substrings;suffix, algorithms;pattern, approximate, characters;dynamic, characters;labeling, characters;string, complexity;indexing;parallel, construction;computational, data, dictionary, dynamic, indexing;efficient, matching;deleted, matching;dynamic, matching;efficient, matching;inserted, matching;string, matching;tree, paradigm;optimal, Parallel, pattern, PROCESSING, string, structures;, text, tree},
	doi = {10.1109/SFCS.1996.548491},
	author = {Sahinalp,S. C and Vishkin, Uzi}
}
