scholarly journals Machine learning with a reduced dimensionality representation of comprehensive Pentacam tomography parameters to identify subclinical keratoconus

2021 ◽  
Vol 138 ◽  
pp. 104884
Author(s):  
Ke Cao ◽  
Karin Verspoor ◽  
Elsie Chan ◽  
Mark Daniell ◽  
Srujana Sahebjada ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reduced Dimensionality

Performance of Various Machine Learning Classifiers on Small Datasets with Varying Dimensionalities: A Study

2016 ◽  
Vol 1 (1) ◽  
pp. 30-35 ◽  
Author(s):  
Sahil Sharma ◽  
Vinod Sharma
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Supervised Learning ◽  
Predictive Accuracy ◽  
Ensemble Method ◽  
Reduced Dimensionality ◽  
Linear Discriminant ◽  
Machine Learning Classifiers ◽  
Learning Technique ◽  
Better Than

Classification is an important supervised learning technique that is used by many applications. An important factor on which the performance of a classifier depends is the size of the dataset using which the classifier is going to be trained. In this manuscript the authors analyzed five different classification techniques (namely decision trees, KNN, SVM, linear discriminant and Ensemble method) in terms of AUC and predictive accuracy when trained using small datasets with different dimensionalities. The study was done using a dataset with 24 features and 400 instances (samples). The results showed that in general ensemble method (using boosted trees) performed better than others but its performance degraded a bit with reduced dimensionality.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques
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