Toward supervised anomaly detection
Anomaly detection is being regarded as an unsupervised learning task as anomalies stem
from adversarial or unlikely events with unknown distributions. However, the predictive
performance of purely unsupervised anomaly detection often fails to match the required …
from adversarial or unlikely events with unknown distributions. However, the predictive
performance of purely unsupervised anomaly detection often fails to match the required …
Deep semi-supervised anomaly detection
Deep approaches to anomaly detection have recently shown promising results over shallow
methods on large and complex datasets. Typically anomaly detection is treated as an
unsupervised learning problem. In practice however, one may have---in addition to a large …
methods on large and complex datasets. Typically anomaly detection is treated as an
unsupervised learning problem. In practice however, one may have---in addition to a large …
Active learning for network intrusion detection
Anomaly detection for network intrusion detection is usually considered an unsupervised
task. Prominent techniques, such as one-class support vector machines, learn a
hypersphere enclosing network data, mapped to a vector space, such that points outside of …
task. Prominent techniques, such as one-class support vector machines, learn a
hypersphere enclosing network data, mapped to a vector space, such that points outside of …
Hidden markov anomaly detection
We introduce a new anomaly detection methodology for data with latent dependency
structure. As a particular instantiation, we derive a hidden Markov anomaly detector that
extends the regular one-class support vector machine. We optimize the approach, which is …
structure. As a particular instantiation, we derive a hidden Markov anomaly detector that
extends the regular one-class support vector machine. We optimize the approach, which is …
Support Vector Data Descriptions and -Means Clustering: One Class?
We present ClusterSVDD, a methodology that unifies support vector data descriptions
(SVDDs) and k-means clustering into a single formulation. This allows both methods to
benefit from one another, ie, by adding flexibility using multiple spheres for SVDDs and …
(SVDDs) and k-means clustering into a single formulation. This allows both methods to
benefit from one another, ie, by adding flexibility using multiple spheres for SVDDs and …
Feature importance measure for non-linear learning algorithms
Complex problems may require sophisticated, non-linear learning methods such as kernel
machines or deep neural networks to achieve state of the art prediction accuracies.
However, high prediction accuracies are not the only objective to consider when solving …
machines or deep neural networks to achieve state of the art prediction accuracies.
However, high prediction accuracies are not the only objective to consider when solving …
Porosity estimation by semi-supervised learning with sparsely available labeled samples
LA Lima, N Görnitz, LE Varella, M Vellasco… - Computers & …, 2017 - Elsevier
This paper addresses the porosity estimation problem from seismic impedance volumes and
porosity samples located in a small group of exploratory wells. Regression methods, trained
on the impedance as inputs and the porosity as output labels, generally suffer from …
porosity samples located in a small group of exploratory wells. Regression methods, trained
on the impedance as inputs and the porosity as output labels, generally suffer from …
Active and semi-supervised data domain description
Data domain description techniques aim at deriving concise descriptions of objects
belonging to a category of interest. For instance, the support vector domain description
(SVDD) learns a hypersphere enclosing the bulk of provided unlabeled data such that points …
belonging to a category of interest. For instance, the support vector domain description
(SVDD) learns a hypersphere enclosing the bulk of provided unlabeled data such that points …
[PDF][PDF] Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation.
We present a novel regularization-based Multitask Learning (MTL) formulation for Structured
Output (SO) prediction for the case of hierarchical task relations. Structured output prediction
often leads to difficult inference problems and hence requires large amounts of training data …
Output (SO) prediction for the case of hierarchical task relations. Structured output prediction
often leads to difficult inference problems and hence requires large amounts of training data …
Minimizing trust leaks for robust sybil detection
J Höner, S Nakajima, A Bauer… - International …, 2017 - proceedings.mlr.press
Sybil detection is a crucial task to protect online social networks (OSNs) against intruders
who try to manipulate automatic services provided by OSNs to their customers. In this paper,
we first discuss the robustness of graph-based Sybil detectors SybilRank and Integro and …
who try to manipulate automatic services provided by OSNs to their customers. In this paper,
we first discuss the robustness of graph-based Sybil detectors SybilRank and Integro and …