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The Improved Relevance Voxel Machine
Melanie Ganz, Mert Sabuncu, Koen Van Leemput
Department of Applied Mathematics and Computer Science
Visual Computing
Massachusetts General Hospital
Research output
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Book/Report
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Report
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Research
276
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Dive into the research topics of 'The Improved Relevance Voxel Machine'. Together they form a unique fingerprint.
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Engineering
Marginals
100%
Sparse Bayesian Learning
100%
Likelihood Function
100%
Optimisation Procedure
50%
Gaussians
50%
Feature Extraction
50%
Basis Function
50%
Free Parameter
50%
Sparse Signal
50%
Selection Algorithm
50%
Compressed Sensing
50%
Objective Function
50%
Regularization
50%
Cost Function
50%
Linear Combination
50%
Sparsity
50%
Fixed Points
50%
Computer Science
Bayesian Learning
100%
Likelihood Function
66%
Marginal Likelihood
66%
Machine Learning
33%
Sparsity
33%
Feature Selection
33%
Compressed Sensing
33%
Regularization Term
33%
Influence Model
33%
Optimization Algorithm
33%
Basis Function
33%
Free Parameter
33%
Linear Combination
33%
Objective Function
33%
Fixed Points
33%
Analytic Expression
33%
Mathematics
Voxel
100%
Bayesian
30%
Marginal Likelihood Function
20%
Objective Function
10%
Regularization
10%
Linear Combination
10%
Model Predict
10%
Regression Model
10%
Cost Function
10%
Fixed Points
10%
Analytic Expression
10%
Compute Prediction
10%
Current Form
10%
Gaussian Distribution
10%
Basis Function
10%
Free Parameter
10%
Keyphrases
Greedy Optimization
66%
Trailblazer
33%
Greedy Forward
33%
Fixed-point Optimization
33%
Regression Weights
33%
Age Regression
33%
Sparse Signal Recovery
33%
Economics, Econometrics and Finance
Bayesian
100%
Machine Learning
33%
Cost Function
33%
Chemical Engineering
Learning System
100%