Dynamic programming | Dimension | Numerical analysis
The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces that do not occur in low-dimensional settings such as the three-dimensional physical space of everyday experience. The expression was coined by Richard E. Bellman when considering problems in dynamic programming. Dimensionally cursed phenomena occur in domains such as numerical analysis, sampling, combinatorics, machine learning, data mining and databases. The common theme of these problems is that when the dimensionality increases, the volume of the space increases so fast that the available data become sparse. In order to obtain a reliable result, the amount of data needed often grows exponentially with the dimensionality. Also, organizing and searching data often relies on detecting areas where objects form groups with similar properties; in high dimensional data, however, all objects appear to be sparse and dissimilar in many ways, which prevents common data organization strategies from being efficient. (Wikipedia).
What is the curse of dimensionality?
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From playlist Quick Machine Learning Concepts
Is the Curse of Dimensionality the same as overfitting?
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From playlist Quick Machine Learning Concepts
How can we mitigate the curse of dimensionality?
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From playlist Quick Machine Learning Concepts
Curse of Dimensionality - EXPLAINED!
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From playlist Machine Learning 101
The curse of dimensionality. Or is it a blessing?
#MachineLearning often deals with high dimensional data or representations. Are many dimensions a blessing or a curse? Possibly a blurse? π ββββββββββββββββββββββββββ π₯ Optionally, pay us a coffee to boost our Coffee Bean production! β Patreon: https://www.patreon.com/AICoffeeBreak Ko-fi
From playlist Dimensionality reduction. The basics.
Which machine learning models are most affected by curse of dimensionality?
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From playlist Quick Machine Learning Concepts
What is a dimension? In 3D...and 2D... and 1D
1D - it's the new 3D! Tweet it - http://bit.ly/mP3FFo Facebook it - http://on.fb.me/qtTraR minutephysics is now on Google+ - http://bit.ly/qzEwc6 And facebook - http://facebook.com/minutephysics Minute Physics provides an energetic and entertaining view of old and new problems
From playlist MinutePhysics
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From playlist Science Unplugged: Black Holes
The Ultimate Guide to Space-time and Relativity
We live in a universe where things like length, distance, and time are all relative and that can lead to strange paradoxes if you're not careful. Thankfully, Einstein's special and general relativity have rules to obey and useful tools like space-time diagrams to help you avoid such proble
From playlist Einstein's Relativity
Ivan Yegorov: "Attenuation of the curse of dimensionality in continuous-time nonlinear optimal f..."
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop I: High Dimensional Hamilton-Jacobi Methods in Control and Differential Games "Attenuation of the curse of dimensionality in continuous-time nonlinear optimal feedback stabilization problems" Ivan Yegorov, North Dakota State University
From playlist High Dimensional Hamilton-Jacobi PDEs 2020
SN Partial Differential Equations and Applications Webinar - Arnulf Jentzen
Join Arnulf Jentzen of University of MΓΌnster as he proves that suitable deep neural network approximations do indeed overcome the curse of dimensionality in the case of a general class of semilinear parabolic PDEs and thereby proves, for the first time, that a general semilinear parabolic
From playlist Talks of Mathematics MΓΌnster's reseachers
Edward Ionides: Island filters for inference on metapopulation dynamics
Low-dimensional compartment models for biological systems can be fitted to time series data using Monte Carlo particle filter methods. As dimension increases, for example when analyzing a collection of spatially coupled populations, particle filter methods rapidly degenerate. We show that
From playlist Probability and Statistics
05b Machine Learning: Curse of Dimensionality
A lecture on the curse of dimensionality. Motivation for feature selection and dimensionality reduction. This is an undergraduate / graduate course that I teach once a year at The University of Texas at Austin. We build from fundamental spatial / subsurface, geoscience / engineering model
From playlist Machine Learning
Overcoming the Curse of Dimensionality and Mode Collapse - Ke Li
Workshop on Theory of Deep Learning: Where next? Topic: Overcoming the Curse of Dimensionality and Mode Collapse Speaker: Ke Li Affiliation: University of California, Berkeley Date: October 15, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
Curse of Dimensionality : Data Science Basics
Why do we run into BIG problems with a BIG number of variables?
From playlist Data Science Basics
08 Machine Learning: Dimensionality Reduction
A lecture on dimensionality reduction through feature selection and feature projection. Includes curse of dimensionality and feature selection review from lecture 5 and summary of methods for feature projection.
From playlist Machine Learning