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Federal
Sparse Solutions for Single Class SVMs: A Bi-Criterion Approach
National Aeronautics and Space Administration —
In this paper we propose an innovative learning algorithm - a variation of One-class Support Vector Machines (SVMs) learning algorithm to produce sparser solutions... -
Federal
Distributed Anomaly Detection using 1-class SVM for Vertically Partitioned Data
National Aeronautics and Space Administration —
There has been a tremendous increase in the volume of sensor data collected over the last decade for different monitoring tasks. For example, petabytes of earth... -
Federal
PROBABILITY CALIBRATION BY THE MINIMUM AND MAXIMUM PROBABILITY SCORES IN ONE-CLASS BAYES LEARNING FOR ANOMALY DETECTION
National Aeronautics and Space Administration —
PROBABILITY CALIBRATION BY THE MINIMUM AND MAXIMUM PROBABILITY SCORES IN ONE-CLASS BAYES LEARNING FOR ANOMALY DETECTION GUICHONG LI, NATHALIE JAPKOWICZ, IAN HOFFMAN,... -
Federal
Pseudo-Label Generation for Multi-Label Text Classification
National Aeronautics and Space Administration —
With the advent and expansion of social networking, the amount of generated text data has seen a sharp increase. In order to handle such a huge volume of text data,... -
Federal
Classification of Mars Terrain Using Multiple Data Sources
National Aeronautics and Space Administration —
Classification of Mars Terrain Using Multiple Data Sources Alan Kraut1, David Wettergreen1 ABSTRACT. Images of Mars are being collected faster than they can be... -
Federal
Discriminative Mixed-Membership Models
National Aeronautics and Space Administration —
Although mixed-membership models have achieved great success in unsupervised learning, they have not been widely applied to classification problems. In this paper, we... -
Federal
MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING
National Aeronautics and Space Administration —
MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING MOHAMMAD SALIM AHMED, LATIFUR KHAN, NIKUNJ OZA, AND MANDAVA RAJESWARI Abstract.... -
Federal
nu-Anomica algorithm
National Aeronautics and Space Administration —
One-class nu-Support Vector machine (SVMs) learning technique maps the input data into a much higher dimensional space and then uses a small portion of the training... -
Federal
nu-Anomica: A Fast Support Vector Based Anomaly Detection Technique
National Aeronautics and Space Administration —
In this paper we propose $\nu$-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the... -
Federal
Fault Adaptive Control of Overactuated Systems Using Prognostic Estimation
National Aeronautics and Space Administration —
Most fault adaptive control research addresses the preservation of system stability or functionality in the presence of a specific failure (fault). This paper... -
Federal
USAGE OF DISSIMILARITY MEASURES AND MULTIDIMENSIONAL SCALING FOR LARGE SCALE SOLAR DATA ANALYSIS
National Aeronautics and Space Administration —
USAGE OF DISSIMILARITY MEASURES AND MULTIDIMENSIONAL SCALING FOR LARGE SCALE SOLAR DATA ANALYSIS Juan M Banda, Rafal Anrgyk ABSTRACT: This work describes the... -
Federal
Novel Methods for Predicting Photometric Redshifts
National Aeronautics and Space Administration —
We calculate photometric redshifts from the Sloan Digital Sky Survey Main Galaxy Sample, The Galaxy Evolution Explorer All Sky Survey, and The Two Micron All Sky... -
Federal
DXC'09 Framework
National Aeronautics and Space Administration —
The DXC Framework is a collection of programs and APIs for running and evaluating diagnostic algorithms (DAs). It is complementary to system XML catalogs and... -
Federal
Comparative Analysis of Data-Driven Anomaly Detection Methods
National Aeronautics and Space Administration —
This paper provides a review of three different advanced machine learning algorithms for anomaly detection in continuous data streams from a ground-test firing of a... -
Federal
ν-Anomica: A Fast Support Vector based Novelty Detection Technique
National Aeronautics and Space Administration —
In this paper we propose ν-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the benchmark... -
Federal
Anomica: Fast Support Vector Based Novelty Detection
National Aeronautics and Space Administration —
In this paper we propose ν-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the benchmark... -
Federal
Communication Optimizations for a Wireless Distributed Prognostic Framework
National Aeronautics and Space Administration —
Distributed architecture for prognostics is an essential step in prognostic research in order to enable feasible real-time system health management. Communication... -
Federal
Deterministic Compilation of Temporal Safety Properties in Explicit State Model Checking
National Aeronautics and Space Administration —
The translation of temporal logic specifications constitutes an essen- tial step in model checking and a major influence on the efficiency of formal verification via... -
Federal
MKAD (Open Sourced Code)
National Aeronautics and Space Administration —
The Multiple Kernel Anomaly Detection (MKAD) algorithm is designed for anomaly detection over a set of files. It combines multiple kernels into a single optimization... -
Federal
Classification 11 recent views
National Aeronautics and Space Administration —
A supervised learning task involves constructing a mapping from an input data space (normally described by several features) to an output space. A set of training...