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2,453
Detecting Features in Spatial Point Processes with . . .
, 1995
"... We consider the problem of detecting features in spatial point processes in the presence of substantial clutter. One example is the detection of mine elds using reconnaissance aircraft images that erroneously identify many objects that are not mines. Another is the detection of seismic faults on the ..."
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Cited by 127 (31 self)
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We consider the problem of detecting features in spatial point processes in the presence of substantial clutter. One example is the detection of mine elds using reconnaissance aircraft images that erroneously identify many objects that are not mines. Another is the detection of seismic faults
An Online Kernel Change Detection Algorithm
, 2004
"... A number of abrupt change detection methods have been proposed in the past, among which are efficient modelbased techniques such as the Generalized Likelihood Ratio (GLR) test. We consider the case where no accurate nor tractable model can be found, using a modelfree approach, called Kernel chang ..."
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Cited by 76 (12 self)
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A number of abrupt change detection methods have been proposed in the past, among which are efficient modelbased techniques such as the Generalized Likelihood Ratio (GLR) test. We consider the case where no accurate nor tractable model can be found, using a modelfree approach, called Kernel
A monte carlo algorithm for fast projective clustering
 In Proceedings of the 2002 ACM SIGMOD International conference on Management of data
, 2002
"... We propose a mathematical formulation for the notion of optimal projective cluster, starting from natural requirements on the density of points in subspaces. This allows us to develop a Monte Carlo algorithm for iteratively computing projective clusters. We prove that the computed clusters are good ..."
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Cited by 104 (1 self)
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with high probability. We implemented a modified version of the algorithm, using heuristics to speed up computation. Our extensive experiments show that our method is significantly more accurate than previous approaches. In particular, we use our techniques to build a classifier for detecting rotated human
WaldBoost  Learning for Time Constrained Sequential Detection
 Proc. of the Conference on Computer Vision and Pattern Recognition
, 2005
"... In many computer vision classification problems, both the error and time characterizes the quality of a decision. We show that such problems can be formalized in the framework of sequential decisionmaking. If the false positive and false negative error rates are given, the optimal strategy in terms ..."
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Cited by 106 (2 self)
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ordered measurements and (ii) known joint probability density functions. We propose an algorithm with near optimal time and error rate tradeoff, called WaldBoost, which integrates the AdaBoost algorithm for measurement selection and ordering and the joint probability density estimation with the optimal
Open Babel: An open chemical toolbox
, 2011
"... Background: A frequent problem in computational modeling is the interconversion of chemical structures between different formats. While standard interchange formats exist (for example, Chemical Markup Language) and de facto standards have arisen (for example, SMILES format), the need to interconvert ..."
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Cited by 96 (1 self)
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algorithms, from partial charge assignment and aromaticity detection, to bond order perception and canonicalization. We detail the implementation of Open Babel, describe key advances in the 2.3 release, and outline a variety of uses both in terms of software products and scientific research, including
A new method to detect related function among proteins independent of sequence and fold
, 2002
"... A new method has been developed to detect functional relationships among proteins independent of a given sequence or fold homology. It is based on the idea that protein function is intimately related to the recognition and subsequent response to the binding of a substrate or an endogenous ligand in ..."
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Cited by 122 (6 self)
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of related cavities of decreasing similarity is detected based on a clique detection algorithm. The detected similarity is ranked according to propertybased surface patches shared in common by the different clique solutions. The approach either retrieves protein cavities accommodating the same (e.g. co
Feature Extraction and Normalization Algorithms for HighDensity Oligonucleotide Gene Expression Array Data
 J. CELL. BIOCHEM. SUPPL.
, 2001
"... Algorithms for performing feature extraction and normalization on highdensity oligonucleotide gene expression arrays, have not been fully explored, and the impact these algorithms have on the downstream analysis is not well understood. Advances in such lowlevel analysis methods are essential to in ..."
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Cited by 71 (8 self)
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Algorithms for performing feature extraction and normalization on highdensity oligonucleotide gene expression arrays, have not been fully explored, and the impact these algorithms have on the downstream analysis is not well understood. Advances in such lowlevel analysis methods are essential
Bulk SpinResonance Quantum Computation
 Computation,” Science
, 1997
"... This article presents a new approach to quantum computing based on using bulk samples rather than isolated degrees of freedom. The problem, of course, is that such samples microscopically are in a thermal distribution of states, and it is impractical to hope to cool macroscopic materials to their gr ..."
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Cited by 118 (6 self)
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's large density matrix a perturbation that acts exactly like a much smaller dimensional effective pure state. We then show how quantum computation can be performed using this ensemble system in such a way that the result is deterministic and can be read out efficiently. One great advantage
Densityconnected subspace clustering for highdimensional data
 IN: PROC. SDM. (2004
, 2004
"... Several application domains such as molecular biology and geography produce a tremendous amount of data which can no longer be managed without the help of efficient and effective data mining methods. One of the primary data mining tasks is clustering. However, traditional clustering algorithms often ..."
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Cited by 68 (14 self)
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the concept of densityconnectivity underlying the algorithm DBSCAN [EKSX96], SUBCLU is based on a formal clustering notion. In contrast to existing gridbased approaches, SUBCLU is able to detect arbitrarily shaped and positioned clusters in subspaces. The monotonicity of densityconnectivity is used
A classification framework for anomaly detection
 J. Machine Learning Research
, 2005
"... One way to describe anomalies is by saying that anomalies are not concentrated. This leads to the problem of finding level sets for the data generating density. We interpret this learning problem as a binary classification problem and compare the corresponding classification risk with the standard p ..."
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Cited by 71 (6 self)
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performance measure for the density level problem. In particular it turns out that the empirical classification risk can serve as an empirical performance measure for the anomaly detection problem. This allows us to compare different anomaly detection algorithms empirically, i.e. with the help of a test set
Results 11  20
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2,453