Results 1 - 10 of 140

Efficient time series matching by wavelets

by Kin-pong Chan, Ada Wai-chee Fu - Proc. of 15th Int'l Conf. on Data Engineering , 1999
"... Time series stored as feature vectors can be indexed by multidimensional index trees like R-Trees for fast retrieval. Due to the dimensionality curse problem, transformations are applied to time series to reduce the number of dimensions of the feature vectors. Different transformations like Discrete ..."
Abstract - Cited by 286 (1 self) - Add to MetaCart
. In this paper, we propose to use Haar Wavelet Transform for time series indexing. The major contributions are: (1) we show that Euclidean distance is preserved in the Haar transformed domain and no false dismissal will occur, (2) we show that Haar transform can outperform DFT through experiments, (3) a new

Evidence for infants’ understanding of false beliefs should not be dismissed. Trends Cogn

by Gergely Csibra, Victoria Southgate - Sci , 2006
"... In their response to Leslie [1], Ruffman & Perner (R&P) reiterate their position that there is no need to explain Onishi & Baillargeon’s (O&B) recent findings [2] with 15-month-olds in terms of attributing false beliefs (FB). Here we put forward three reasons why their points do not ..."
Abstract - Cited by 17 (3 self) - Add to MetaCart
In their response to Leslie [1], Ruffman & Perner (R&P) reiterate their position that there is no need to explain Onishi & Baillargeon’s (O&B) recent findings [2] with 15-month-olds in terms of attributing false beliefs (FB). Here we put forward three reasons why their points do

Properties of embedding methods for similarity searching in metric spaces

by Gísli R. Hjaltason, Hanan Samet - PAMI , 2003
"... Complex data types—such as images, documents, DNA sequences, etc.—are becoming increasingly important in modern database applications. A typical query in many of these applications seeks to find objects that are similar to some target object, where (dis)similarity is defined by some distance functi ..."
Abstract - Cited by 109 (5 self) - Add to MetaCart
.e., there are no false dismissals and, hence, the correct result is reported). Particular attention is paid to the SparseMap, FastMap, and MetricMap embedding methods. SparseMap is a variant of Lipschitz embeddings, while FastMap and MetricMap are inspired by dimension reduction methods for Euclidean spaces (using KLT

Fast Nearest Neighbor Search in Medical Image Databases

by Flip Korn, Nikolaos Sidiropoulos, Christos Faloutsos, Eliot Siegel, Zenon Protopapas - In Proceedings of the Int. Conf. on Very Large Data Bases , 1996
"... We examine the problem of finding similar tumor shapes. Starting from a natural similarity function (the so-called `max morphological distance'), we show how to lower-bound it and how to search for nearest neighbors in large collections of tumor-like shapes. Specifically, we use state-of- ..."
Abstract - Cited by 124 (10 self) - Add to MetaCart
dismissals for range queries. In addition, we present a nearest neighbor algorithm that also guarantees no false dismissals. Finally, we implemented the method and tested it against a testbed of realistic tumor shapes, using an established tumor-growth model of Murray Eden[13]. The experiments

Indexing multi-dimensional time-series with support for multiple distance measures

by Michail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn Keogh , 2003
"... Although most time-series data mining research has concentrated on providing solutions for a single distance function, in this work we motivate the need for a single index structure that can support multiple distance measures. Our specific area of interest is the efficient retrieval and analysis of ..."
Abstract - Cited by 121 (15 self) - Add to MetaCart
other researchers have advocated one or other of these similarity measures, a major contribution of our work is the ability to support all these measures without the need to restructure the index. Our framework guarantees no false dismissals and can also be tailored to provide much faster response time

Update TRENDS in Cognitive Sciences Vol.10 No.1 January 20064should not be dismissed

by Gergely Csibra, Victoria Southgate , 2005
"... false beliefs al ..."
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false beliefs al

Similarity Searching in Medical Image DataBases

by Euripides G. M. Petrakis, Christos Faloutsos , 1997
"... We propose a method to handle approximate searching by image content in medical image databases. Image content is represented by attributed relational graphs holding features of objects and relationships between objects. The method relies on the assumption that a fixed number of "labeled" ..."
Abstract - Cited by 107 (8 self) - Add to MetaCart
is approximate so that all images up to a prespecified degree of similarity (tolerance) are retrieved, (b) it has no "false dismissals" (i.e., all images qualifying query selection criteria are retrieved) and (c) it is much faster than sequential scanning for searching in the main memory

unknown title

by unknown authors
"... Abstract. The standard IGEC approach to detection of gravitational waves with many detectors is simple time coincidence search. We discuss the problems of false alarm and false dismissal assessment, both in the case of stationary and non-stationary noise. The significance of any cumulative excess of ..."
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Abstract. The standard IGEC approach to detection of gravitational waves with many detectors is simple time coincidence search. We discuss the problems of false alarm and false dismissal assessment, both in the case of stationary and non-stationary noise. The significance of any cumulative excess

unknown title

by unknown authors
"... Abstract. The standard IGEC approach to detection of gravitational waves with many detectors is simple time coincidence search. We discuss the problems of false alarm and false dismissal assessment, both in the case of stationary and non-stationary noise. The significance of any cumulative excess of ..."
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Abstract. The standard IGEC approach to detection of gravitational waves with many detectors is simple time coincidence search. We discuss the problems of false alarm and false dismissal assessment, both in the case of stationary and non-stationary noise. The significance of any cumulative excess

An Index-Based Approach for Similarity Search Supporting Time Warping in Large Sequence Databases

by Sang-wook Kim, Sanghyun Park, Wesley W. Chu - In ICDE , 2001
"... This paper discusses an effective processing of similarity search that supports time warping in large sequence databases. Time warping enables finding sequences with similar patterns even when they are of different lengths. Previous methods for processing similarity search that supports time warp ..."
Abstract - Cited by 58 (3 self) - Add to MetaCart
warping fail to employ multi-dimensional indexes without false dismissal since the time warping distance does not satisfy the triangular inequality. They have to scan all the database, thus suffer from serious performance degradation in large databases. Another method that hires the suffix tree, which
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