Novelty detection with multivariate extreme value statistics

Novelty detection with multivariate extreme value statistics

Novelty detection, or one-class classification, aims to determine if data are “normal” with respect to some model of normality constructed using examples of normal system behaviour. If that model is composed of generative probability distributions, the extent of “normality” in the data space can be...

Bibliográfalaš dieđut
Váldodahkkit:	Clifton, D, Hugueny, S, Tarassenko, L
Materiálatiipa:	Journal article
Almmustuhtton:	Springer 2010

Geahča maid

A COMPARISON OF APPROACHES TO MULTIVARIATE EXTREME VALUE THEORY FOR NOVELTY DETECTION
Dahkki: Clifton, D, et al.
Almmustuhtton: (2009)

Novelty detection with multivariate extreme value theory, part I: A numerical approach to multimodal estimation
Dahkki: Clifton, D, et al.
Almmustuhtton: (2009)

NOVELTY DETECTION WITH MULTIVARIATE EXTREME VALUE THEORY, PART II: AN ANALYTICAL APPROACH TO UNIMODAL ESTIMATION
Dahkki: Hugueny, S, et al.
Almmustuhtton: (2009)

NOVELTY DETECTION WITH MULTIVARIATE EXTREME VALUE THEORY, PART I: A NUMERICAL APPROACH TO MULTIMODAL ESTIMATION
Dahkki: Clifton, D, et al.
Almmustuhtton: (2009)

An extreme function theory for novelty detection
Dahkki: Clifton, D, et al.
Almmustuhtton: (2012)

Probabilistic Patient Monitoring with Multivariate, Multimodal Extreme Value Theory
Dahkki: Hugueny, S, et al.
Almmustuhtton: (2011)

Probabilistic patient monitoring using extreme value theory: A multivariate, multimodal methodology for detecting patient deterioration
Dahkki: Hugueny, S, et al.
Almmustuhtton: (2010)

Novelty detection with extreme value theory in vital-sign monitoring
Dahkki: Hugueny, S, et al.
Almmustuhtton: (2013)

Bayesian extreme value statistics for novelty detection in gas-turbine engines
Dahkki: Clifton, D, et al.
Almmustuhtton: (2008)

Extending the generalised Pareto distribution for novelty detection in high-dimensional spaces
Dahkki: Clifton, D, et al.
Almmustuhtton: (2013)

A review of novelty detection
Dahkki: Pimentel, M, et al.
Almmustuhtton: (2014)

PINNING THE TAIL ON THE DISTRIBUTION: A MULTIVARIATE EXTENSION TO THE GENERALISED PARETO DISTRIBUTION
Dahkki: Clifton, D, et al.
Almmustuhtton: (2011)

Automated novelty detection in industrial systems
Dahkki: Clifton, D, et al.
Almmustuhtton: (2008)

Extreme value statistics for novelty detection in biomedical data processing
Dahkki: Roberts, S
Almmustuhtton: (2000)

Extreme value statistics for novelty detection in biomedical signal processing
Dahkki: Roberts, S, et al.
Almmustuhtton: (2000)

Novelty detection in large-vehicle turbocharger operation
Dahkki: Clifton, D, et al.
Almmustuhtton: (2007)

Learning shape for jet engine novelty detection
Dahkki: Clifton, D, et al.
Almmustuhtton: (2006)

Probabilistic novelty detection with support vector machines
Dahkki: Clifton, L, et al.
Almmustuhtton: (2014)

A framework for novelty detection in jet engine vibration data
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Almmustuhtton: (2007)

Novelty Detection for Identifying Deterioration in Emergency Department Patients
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Almmustuhtton: (2011)

Application of an intuitive novelty metric for jet engine condition monitoring
Dahkki: Clifton, D, et al.
Almmustuhtton: (2006)

Point process models for novelty detection on spatial point patterns and their extremes
Dahkki: Luca, SE, et al.
Almmustuhtton: (2018)

A PROBABILISTIC RESOURCE ALLOCATING NETWORK FOR NOVELTY DETECTION
Dahkki: Roberts, S, et al.
Almmustuhtton: (1994)

Novelty detection for the identification of abnormalities
Dahkki: Tarassenko, L, et al.
Almmustuhtton: (2000)

On-line Novelty Detection Using the Kalman Filter and Extreme Value Theory
Dahkki: Lee, H, et al.
Almmustuhtton: (2008)

Choosing an appropriate model for novelty detection
Dahkki: Nairac, A, et al.
Almmustuhtton: (1997)

Combined support vector novelty detection for multi-channel combustion data
Dahkki: Clifton, L, et al.
Almmustuhtton: (2007)

Static and dynamic novelty detection methods for jet engine health monitoring.
Dahkki: Hayton, P, et al.
Almmustuhtton: (2007)

Static and dynamic novelty detection methods for jet engine health monitoring
Dahkki: Hayton, P, et al.
Almmustuhtton: (2007)

Support vector novelty detection applied to Jet Engine vibration spectra
Dahkki: Hayton, P, et al.
Almmustuhtton: (2001)

Vertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theory
Dahkki: J. Steinheuer, et al.
Almmustuhtton: (2020-04-01)

How should novelty be valued in science?
Dahkki: Barak A Cohen
Almmustuhtton: (2017-07-01)

Estimating strategies for multiparameter Multivariate Extreme Value copulas
Dahkki: G. Salvadori, et al.
Almmustuhtton: (2011-01-01)

Extreme Value Statistics in Silicon Photonics
Dahkki: D. Borlaug, et al.
Almmustuhtton: (2009-01-01)

Database retrieval for novelty detection
Dahkki: Ong, Chun Lin.
Almmustuhtton: (2009)

Mixture of hyperspheres for novelty detection
Dahkki: Duy Nguyen, et al.
Almmustuhtton: (2016-06-01)

The Experience of Novelty and the Novelty of Experience
Dahkki: Liubov Skavronskaya, et al.
Almmustuhtton: (2020-02-01)

Use of neural networks and novelty to determine periods of physiological instability prior to clinical events
Dahkki: Braithwaite, E, et al.
Almmustuhtton: (2003)

Detecting Extreme Values with Order Statistics in Samples from Continuous Distributions
Dahkki: Lorentz Jäntschi
Almmustuhtton: (2020-02-01)

Optimising classifiers for the detection of physiological deterioration in patient vital-sign data
Dahkki: Khalid, S, et al.
Almmustuhtton: (2011)