Skip to main navigation
Skip to search
Skip to main content
Aston Research Explorer Home
Help & FAQ
Home
Research units
Profiles
Research Outputs
Datasets
Student theses
Activities
Press/Media
Prizes
Equipment
Search by expertise, name or affiliation
Training with noise is equivalent to Tikhonov regularization
Christopher M. Bishop
Aston University
Research output
:
Contribution to journal
›
Article
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Training with noise is equivalent to Tikhonov regularization'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Error Function
100%
Training with Noise
100%
Tikhonov Regularization
100%
Regularization Term
75%
Neural Network
25%
Network Training
25%
Learning Algorithm
25%
Network Mapping
25%
Tikhonov
25%
Regularizer
25%
Positive Definite Forms
25%
Error Minimization
25%
First Derivative
25%
Sum Square Error
25%
Bounded below
25%
Generalization Performance
25%
Second Derivative
25%
Direct Minimization
25%
Computer Science
Error Function
100%
Tikhonov Regularization
100%
Regularization Term
75%
Neural Network
25%
Learning Algorithm
25%
Regularization
25%
Generalization Performance
25%
Positive Definite
25%
Direct Minimisation
25%
Mathematics
Regularization
100%
Error Function
80%
Neural Network
20%
Sum of Squares
20%
Positive Definite
20%
Square Error
20%
Input Data
20%
Engineering
Regularization
100%
Error Function
80%
Network Training
20%
Learning Algorithm
20%
Input Data
20%
Positive Definite
20%
Square Error
20%
Physics
Neural Network
100%