What are fusion algorithms in Data Science/ ML and how to implement one
I am not aware of the correct terminology, but what I'm looking for is to mix two feature vectors in a classification model (where the features might be related to a class or entity we are classifying against, an example would be pupil movement and eyeball movement of an individual in a verification system).
A very trivial implementation would be (and the only one I'm aware of) would be to classify both the attribute vectors individually and then using some weight for each decision make a conclusion.
I have 'heard' there is a class of algorithms called 'fusion' algorithms to essentially mix the two attributes into one. How can I mix the two attributes into one feature vector? Is it going be helpful in my case of identity verification classification problem?
machine-learning data-modeling modeling
asked Nov 11 at 18:02
DaveIdito
259210
add a comment |
I am not aware of the correct terminology, but what I'm looking for is to mix two feature vectors in a classification model (where the features might be related to a class or entity we are classifying against, an example would be pupil movement and eyeball movement of an individual in a verification system).
A very trivial implementation would be (and the only one I'm aware of) would be to classify both the attribute vectors individually and then using some weight for each decision make a conclusion.
I have 'heard' there is a class of algorithms called 'fusion' algorithms to essentially mix the two attributes into one. How can I mix the two attributes into one feature vector? Is it going be helpful in my case of identity verification classification problem?
machine-learning data-modeling modeling
asked Nov 11 at 18:02
DaveIdito
259210
add a comment |
I am not aware of the correct terminology, but what I'm looking for is to mix two feature vectors in a classification model (where the features might be related to a class or entity we are classifying against, an example would be pupil movement and eyeball movement of an individual in a verification system).
A very trivial implementation would be (and the only one I'm aware of) would be to classify both the attribute vectors individually and then using some weight for each decision make a conclusion.
I have 'heard' there is a class of algorithms called 'fusion' algorithms to essentially mix the two attributes into one. How can I mix the two attributes into one feature vector? Is it going be helpful in my case of identity verification classification problem?
machine-learning data-modeling modeling
asked Nov 11 at 18:02
DaveIdito
259210
I am not aware of the correct terminology, but what I'm looking for is to mix two feature vectors in a classification model (where the features might be related to a class or entity we are classifying against, an example would be pupil movement and eyeball movement of an individual in a verification system).
A very trivial implementation would be (and the only one I'm aware of) would be to classify both the attribute vectors individually and then using some weight for each decision make a conclusion.
I have 'heard' there is a class of algorithms called 'fusion' algorithms to essentially mix the two attributes into one. How can I mix the two attributes into one feature vector? Is it going be helpful in my case of identity verification classification problem?
machine-learning data-modeling modeling
machine-learning data-modeling modeling
asked Nov 11 at 18:02
DaveIdito
259210
asked Nov 11 at 18:02
DaveIdito
259210
asked Nov 11 at 18:02
DaveIdito
259210
asked Nov 11 at 18:02
DaveIdito
259210
asked Nov 11 at 18:02
DaveIdito
259210
259210
add a comment |
add a comment |
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