Anatomical multiatlas segmentation using local texture statistical properties for matching descriptor with machine learning

2021 ◽  
Author(s):  
Ali Ould Kradda ◽  
Abdelghani Ghomari ◽  
Abdennacer Ben Hmed ◽  
Stephane Binczak
Keyword(s):  
Machine Learning ◽  
Statistical Properties ◽  
Local Texture

A new generic method to improve machine learning applications in official statistics

2021 ◽  
pp. 1-16
Author(s):  
Kevin Kloos
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Statistical Properties ◽  
Machine Learning Algorithms ◽  
Official Statistics ◽  
Academic Literature ◽  
Misclassification Bias ◽  
Squared Error ◽  
Machine Learning Applications ◽  
Applications Of Machine Learning

The use of machine learning algorithms at national statistical institutes has increased significantly over the past few years. Applications range from new imputation schemes to new statistical output based entirely on machine learning. The results are promising, but recent studies have shown that the use of machine learning in official statistics always introduces a bias, known as misclassification bias. Misclassification bias does not occur in traditional applications of machine learning and therefore it has received little attention in the academic literature. In earlier work, we have collected existing methods that are able to correct misclassification bias. We have compared their statistical properties, including bias, variance and mean squared error. In this paper, we present a new generic method to correct misclassification bias for time series and we derive its statistical properties. Moreover, we show numerically that it has a lower mean squared error than the existing alternatives in a wide variety of settings. We believe that our new method may improve machine learning applications in official statistics and we aspire that our work will stimulate further methodological research in this area.

Internet traffic classification based on flows' statistical properties with machine learning

2016 ◽  
Vol 27 (3) ◽  
pp. e1929 ◽  
Author(s):  
Alina Vlăduţu ◽  
Dragoş Comăneci ◽  
Ciprian Dobre
Keyword(s):  
Machine Learning ◽  
Internet Traffic ◽  
Statistical Properties ◽  
Traffic Classification ◽  
Internet Traffic Classification

scholarly journals An epistemic approach to the formal specification of statistical machine learning

2020 ◽  
Author(s):  
Yusuke Kawamoto
Keyword(s):  
Machine Learning ◽  
Formal Specification ◽  
Formal Model ◽  
Kripke Model ◽  
Classification Performance ◽  
Statistical Properties ◽  
Statistical Machine Learning ◽  
Possible World ◽  
Starting Point ◽  
Epistemic Approach

Abstract We propose an epistemic approach to formalizing statistical properties of machine learning. Specifically, we introduce a formal model for supervised learning based on a Kripke model where each possible world corresponds to a possible dataset and modal operators are interpreted as transformation and testing on datasets. Then, we formalize various notions of the classification performance, robustness, and fairness of statistical classifiers by using our extension of statistical epistemic logic. In this formalization, we show relationships among properties of classifiers, and relevance between classification performance and robustness. As far as we know, this is the first work that uses epistemic models and logical formulas to express statistical properties of machine learning, and would be a starting point to develop theories of formal specification of machine learning.

scholarly journals Algorithms for Extracting various Local Texture Features

2022 ◽  
Vol 2161 (1) ◽  
pp. 012067
Author(s):  
B Ashwath Rao ◽  
N Gopalakrishna Kini
Keyword(s):  
Machine Learning ◽  
Texture Feature ◽  
Texture Features ◽  
Image Indexing ◽  
Content Based Image Retrieval ◽  
Texture Feature Extraction ◽  
Global Features ◽  
Memory Space ◽  
Local Texture ◽  
Over Time

Abstract In the machine learning and computer vision domain, images are represented using their features. Color, shape, and texture are some of the prominent types of features. Over time, the local features of an image have gained importance over the global features due to their high discerning ability in localized regions. The texture features are widely used in image indexing and content-based image retrieval. In the last two decades, various local texture features have been formulated. For a complete description of images, effective and efficient features are necessary. In this paper, we provide algorithms for 10 local texture feature extraction. These texture descriptors have been formulated since the year 2015. We have designed algorithms so that they are time efficient and memory space-efficient. We have implemented these algorithms and verified their output correctness.

Machine learning based brain tumour segmentation on limited data using local texture and abnormality

2018 ◽  
Vol 98 ◽  
pp. 39-47 ◽  
Author(s):  
Stijn Bonte ◽  
Ingeborg Goethals ◽  
Roel Van Holen
Keyword(s):  
Machine Learning ◽  
Brain Tumour ◽  
Limited Data ◽  
Local Texture ◽  
Tumour Segmentation ◽  
Brain Tumour Segmentation

Using machine learning to predict statistical properties of non-stationary dynamical processes: System climate,regime transitions, and the effect of stochasticity

2021 ◽  
Vol 31 (3) ◽  
pp. 033149
Author(s):  
Dhruvit Patel ◽  
Daniel Canaday ◽  
Michelle Girvan ◽  
Andrew Pomerance ◽  
Edward Ott
Keyword(s):  
Machine Learning ◽  
Statistical Properties ◽  
Climate Regime ◽  
Dynamical Processes ◽  
Regime Transitions

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.

Effect of thermal annealing on grain boundary chemistry of polycrystalline TiBa2Ca2Cu3Oy films

1994 ◽  
Vol 52 ◽  
pp. 968-969
Author(s):  
Z. L. Wang ◽  
C. L. Briant ◽  
J. DeLuca ◽  
A. Goyal ◽  
D. M. Kroeger ◽  
...  
Keyword(s):  
Grain Boundary ◽  
Weak Link ◽  
Zero Field ◽  
Stabilized Zirconia ◽  
Local Texture ◽  
Transmission Electron ◽  
Axis Alignment ◽  
Boundary Chemistry ◽  
Small Misorientation ◽  
Misorientation Angles

Recent studies have shown that spray-pyrolyzed films of the Tl-1223 compound (TlxBa2Ca2Cu3Oy, with 0.7 < × < 0.95) on polycrystalline yttrium stabilized zirconia substrates can be prepared which have critical current density Jc near 105 A/cm2 at 77 K, in zero field. The films are polycrystalline, have excellent c-axis alignment, and show little evidence of weak-link behavior. Transmission electron microscopy (TEM) studies have shown that most grain boundaries have small misorientation angles. It has been found that the films have a nigh degree of local texture indicative of colonies of similarly oriented grains. It is believed that inter-colony conduction is enhanced by a percolative network of small angle boundaries at colony interfaces. It has also been found that Jc is increased by a factor of 4 - 5 after the films were annealed at 600 °C in oxygen. This study is thus carried out to determine the effect on grain boundary chemistry of the heat treatment.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition
Sign in / Sign up

Export Citation Format

Share Document