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Results 11 - 20
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5,566
A line in the sand: a wireless sensor network for target detection, classification, and tracking
- COMPUTER NETWORKS
, 2004
"... Intrusion detection is a surveillance problem of practical import that is well suited to wireless sensor networks. In this paper, we study the application of sensor networks to the intrusion detection problem and the related problems of classifying and tracking targets. Our approach is based on a de ..."
Abstract
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Cited by 272 (41 self)
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define the target, system, environment, and fault models. Based on the performance requirements of the scenario and the sensing, communication, energy, and computation ability of the sensor network, we explore the design space of sensors, signal processing algorithms, communications, networking
Mining Concept-Drifting Data Streams Using Ensemble Classifiers
, 2003
"... Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud protection, target marketing, network intrusion detection, etc. Conventional knowledge discovery tools are facing two ch ..."
Abstract
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Cited by 280 (37 self)
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classification. Our empirical study shows that the proposed methods have substantial advantage over single-classifier approaches in prediction accuracy, and the ensemble framework is effective for a variety of classification models.
Automated model-based tissue classification of MR images of the brain
, 1999
"... We describe a fully automated method for model-based tissue classification of Magnetic Resonance (MR) images of the brain. The method interleaves classification with estimation of the model parameters, improving the classification at each iteration. The algorithm is able to segment single- and multi ..."
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Cited by 214 (14 self)
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We describe a fully automated method for model-based tissue classification of Magnetic Resonance (MR) images of the brain. The method interleaves classification with estimation of the model parameters, improving the classification at each iteration. The algorithm is able to segment single
Iterative classification of relational data
- Papers of the AAAI-2000 Workshop on Learning Statistical Models From Relational Data
, 2000
"... Relational data offer a unique opportunity for improving the classification accuracy of statistical models. If two objects are related, inferring something about one object can aid inferences about the other. We present an iterative classification procedure that exploits this characteristic of relat ..."
Abstract
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Cited by 162 (15 self)
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inferences about related objects. We evaluate the performance of this approach on a binary classification task. Experiments indicate that iterative classification significantly increases accuracy when compared to a single-pass approach.
Sequence Comparisons Using Multiple Sequences Detect Three Times as Many Remote . . .
, 1998
"... The sequences of related proteins can diverge beyond the point where their relationship can be recognised by pairwise sequence comparisons. In attempts to overcome this limitation, methods have been developed that use as a query, not a single sequence, but sets of related sequences or a representati ..."
Abstract
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Cited by 244 (16 self)
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The sequences of related proteins can diverge beyond the point where their relationship can be recognised by pairwise sequence comparisons. In attempts to overcome this limitation, methods have been developed that use as a query, not a single sequence, but sets of related sequences or a
Modeling human motion using binary latent variables
- Advances in Neural Information Processing Systems
, 2006
"... We propose a non-linear generative model for human motion data that uses an undirected model with binary latent variables and real-valued “visible ” variables that represent joint angles. The latent and visible variables at each time step receive directed connections from the visible variables at th ..."
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Cited by 151 (20 self)
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We propose a non-linear generative model for human motion data that uses an undirected model with binary latent variables and real-valued “visible ” variables that represent joint angles. The latent and visible variables at each time step receive directed connections from the visible variables
Taxonomy of xml schema languages using formal language theory
- EXTREME MARKUP LANGUAGES
, 2001
"... On the basis of regular tree grammars, we present a formal framework for XML schema languages. This framework helps to describe, compare, and implement such schema languages in a rigorous manner. Our main results are as follows: (1) a simple framework to study three classes of tree languages (local, ..."
Abstract
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Cited by 234 (6 self)
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, single-type, and regular); (2) classification and comparison of schema languages (DTD, W3C XML Schema, and RELAX NG) based on these classes; (3) efficient document validation algorithms for these classes; and (4) other grammatical concepts and advanced validation algorithms relevant to an XML model (e
2004): “Endogeneity in Semiparametric Binary Response Models,”Review of Economic Studies
"... This paper develops and implements semiparametric methods for estimating binary response (binary choice) models with continuous endogenous regressors. It extends existing results on semiparametric estimation in single index binary response models to the case of endogenous regressors. It develops a c ..."
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Cited by 157 (8 self)
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This paper develops and implements semiparametric methods for estimating binary response (binary choice) models with continuous endogenous regressors. It extends existing results on semiparametric estimation in single index binary response models to the case of endogenous regressors. It develops a
Classifier Chains for Multi-label Classification
"... Abstract. The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable ..."
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Cited by 162 (13 self)
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Abstract. The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest
Support Vector Machines for Multi-Class Pattern Recognition
, 1999
"... . The solution of binary classification problems using support vector machines (SVMs) is well developed, but multi-class problems with more than two classes have typically been solved by combining independently produced binary classifiers. We propose a formulation of the SVM that enables a multi-cla ..."
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Cited by 207 (6 self)
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. The solution of binary classification problems using support vector machines (SVMs) is well developed, but multi-class problems with more than two classes have typically been solved by combining independently produced binary classifiers. We propose a formulation of the SVM that enables a multi
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