Journal ArticleDOI
From outliers to prototypes: Ordering data
TLDR
Simple and fast methods based on nearest neighbors that order objects from high-dimensional data sets from typical points to untypical points allow us to detect outliers with a performance comparable to or better than other often much more sophisticated methods.About:
This article is published in Neurocomputing.The article was published on 2006-08-01. It has received 102 citations till now. The article focuses on the topics: Cluster analysis & Exploratory data analysis.read more
Citations
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Journal ArticleDOI
Automatic analysis of malware behavior using machine learning
TL;DR: An incremental approach for behavior-based analysis, capable of processing the behavior of thousands of malware binaries on a daily basis is proposed, significantly reduces the run-time overhead of current analysis methods, while providing accurate discovery and discrimination of novel malware variants.
Journal ArticleDOI
Introduction to machine learning for brain imaging.
TL;DR: An accessible and clear introduction to the strengths and also the inherent dangers of machine learning usage in the neurosciences is provided.
Journal ArticleDOI
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff,Jacob R. Kauffmann,Robert A. Vandermeulen,Grégoire Montavon,Wojciech Samek,Marius Kloft,Thomas G. Dietterich,Klaus-Robert Müller +7 more
TL;DR: This review aims to identify the common underlying principles and the assumptions that are often made implicitly by various methods in deep learning, and draws connections between classic “shallow” and novel deep approaches and shows how this relation might cross-fertilize or extend both directions.
Journal ArticleDOI
Towards Zero Training for Brain-Computer Interfacing
TL;DR: By a novel technique, prototypical spatial filters are determined which have better generalization properties compared to single-session filters and can be used in follow-up sessions without the need to recalibrate the system.
Journal ArticleDOI
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff,Jacob R. Kauffmann,Robert A. Vandermeulen,Grégoire Montavon,Wojciech Samek,Marius Kloft,Thomas G. Dietterich,Klaus-Robert Müller +7 more
TL;DR: Deep learning approaches to anomaly detection (AD) have recently improved the state of the art in detection performance on complex data sets, such as large collections of images or text as mentioned in this paper, and led to the introduction of a great variety of new methods.
References
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Book
Introduction to Algorithms
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Journal ArticleDOI
Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
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A global geometric framework for nonlinear dimensionality reduction.
TL;DR: An approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set and efficiently computes a globally optimal solution, and is guaranteed to converge asymptotically to the true structure.
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Basic principles of ROC analysis
TL;DR: ROC analysis is shown to be related in a direct and natural way to cost/benefit analysis of diagnostic decision making and the concepts of "average diagnostic cost" and "average net benefit" are developed and used to identify the optimal compromise among various kinds of diagnostic error.