Kernel mean embedding of distributions: A review and beyond
A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …
recently emerged as a powerful tool for machine learning and statistical inference. The basic …
A review of feature reduction techniques in neuroimaging
B Mwangi, TS Tian, JC Soares - Neuroinformatics, 2014 - Springer
Abstract Machine learning techniques are increasingly being used in making relevant
predictions and inferences on individual subjects neuroimaging scan data. Previous studies …
predictions and inferences on individual subjects neuroimaging scan data. Previous studies …
Optimizing the latent space of generative networks
P Bojanowski, A Joulin, D Lopez-Paz… - ar** using support vector regression
Y Zhang, DY Kimberg, HB Coslett… - Human brain …, 2014 - Wiley Online Library
Lesion analysis is a classic approach to study brain functions. Because brain function is a
result of coherent activations of a collection of functionally related voxels, lesion‐symptom …
result of coherent activations of a collection of functionally related voxels, lesion‐symptom …
[LIBRO][B] Machine learning methods in the environmental sciences: Neural networks and kernels
WW Hsieh - 2009 - books.google.com
Machine learning methods originated from artificial intelligence and are now used in various
fields in environmental sciences today. This is the first single-authored textbook providing a …
fields in environmental sciences today. This is the first single-authored textbook providing a …
Closed-loop reservoir management
JD Jansen, SD Douma, DR Brouwer… - SPE Reservoir …, 2009 - onepetro.org
Closed-loop reservoir management is a combination of model-based optimization and data
assimilation (computer-assisted history matching), also referred to as 'real-time reservoir …
assimilation (computer-assisted history matching), also referred to as 'real-time reservoir …
Stochastic simulation of patterns using distance-based pattern modeling
The advent of multiple-point geostatistics (MPS) gave rise to the integration of complex
subsurface geological structures and features into the model by the concept of training …
subsurface geological structures and features into the model by the concept of training …
Kernel entropy component analysis
R Jenssen - IEEE transactions on pattern analysis and …, 2009 - ieeexplore.ieee.org
We introduce kernel entropy component analysis (kernel ECA) as a new method for data
transformation and dimensionality reduction. Kernel ECA reveals structure relating to the …
transformation and dimensionality reduction. Kernel ECA reveals structure relating to the …
Machine Learning Methods for Property Prediction in Chemoinformatics: Quo Vadis?
This paper is focused on modern approaches to machine learning, most of which are as yet
used infrequently or not at all in chemoinformatics. Machine learning methods are …
used infrequently or not at all in chemoinformatics. Machine learning methods are …