Unbalanced data processing using deep sparse learning technique

2021 ◽  
Author(s):  
Xing Li ◽  
Lei Zhang
Keyword(s):  
Data Processing ◽  
Sparse Learning ◽  
Unbalanced Data ◽  
Learning Technique

scholarly journals Pengenalan Gerak Manusia Menggunakan Algoritma Relevance Vector Machine pada MSRC-12 Dataset

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Vina Ayumi ◽  
Erwin Dwika Putra
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Statistical Learning ◽  
Gesture Recognition ◽  
Learning Theory ◽  
Relevance Vector Machine ◽  
Motion Model ◽  
Communication Tools ◽  
Machine Learning Technique ◽  
Learning Technique

Relevance vector machine is a popular machine learning technique that is motivated by statistical learning theory. RVM can be used for gesture recognition which is one of the communication tools used by humans. This study proposes an experiment using the Relevance Vector Machine (RVM) algorithm on gesture data from Microsoft Research Cambridge-12 (MSRC-12) as a proposed solution to overcome unbalanced problems in data processing. The results of the study are the accuracy for 1-person motion model reaches 100% and the lowest accuracy with 5 people the motion model reaches 96%. Graphically, the more people or models, the lower the algorithm's accuracy.

scholarly journals Unbalanced data processing using oversampling: Machine Learning

2020 ◽  
Vol 175 ◽  
pp. 108-113 ◽  
Author(s):  
Amelec Viloria ◽  
Omar Bonerge Pineda Lezama ◽  
Nohora Mercado-Caruzo
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Unbalanced Data

scholarly journals A Track-Before-Detect Strategy Based on Sparse Data Processing for Air Surveillance Radar Applications

2021 ◽  
Vol 13 (4) ◽  
pp. 662
Author(s):  
Nicomino Fiscante ◽  
Pia Addabbo ◽  
Carmine Clemente ◽  
Filippo Biondi ◽  
Gaetano Giunta ◽  
...  
Keyword(s):  
Data Processing ◽  
Ad Hoc ◽  
Tracking System ◽  
Sparse Data ◽  
Sparse Learning ◽  
Multiple Target ◽  
Novel Approach ◽  
Air Surveillance ◽  
Reference Track ◽  
Track Before Detect

In this paper we consider the tracking problem of a moving target competing against noise and clutter in a surveillance radar scenario. For a single array-antenna multiple-target tracking system and according to the Track-Before-Detect paradigm, we present a novel approach based on a three-stage processing chain that involves the Sparse Learning via Iterative Minimization algorithm, the k-means clustering method and the ad hoc detector by exploiting the sparse nature of the operating scenario. Under the latter assumption, the detection strategy declares the presence of targets subsequently to the retrieval of their corresponding tracks performed by jointly processing the received echoes of multiple consecutive radar scans. Simulation results show that the proposed approach is able to provide good tracking and detection capabilities for different multiple target trajectories with low Signal-to-Interference-plus-Noise ratio and results in providing advantages when compared to a number of other reference Track-Before-Detect strategies based on sparse data processing techniques.

Taxonorics. II. Formats and coding systems for data processing

1970 ◽  
Vol 126 (6) ◽  
pp. 1053-1067 ◽  
Author(s):  
A. R. Feinstein
Keyword(s):  
Data Processing ◽  
Coding Systems

Machine Learning of Motor Vehicle Accident Categories from Narrative Data

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock
Keyword(s):  
Machine Learning ◽  
Bayesian Model ◽  
Keyword Search ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Computer Search ◽  
Vehicle Accident ◽  
Learning Technique ◽  
Expert Ratings ◽  
Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

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