AI News, What are advantages of Artificial Neural Networks over Support Vector Machines? [closed]

What are advantages of Artificial Neural Networks over Support Vector Machines? [closed]

The reason I ask is because it's easy to answer the opposite question: Support Vector Machines are often superior to ANNs because they avoid two major weaknesses of ANNs: (1) ANNs often converge on local minima rather than global minima, meaning that they are essentially 'missing the big picture' sometimes (or missing the forest for the trees) (2) ANNs often overfit if training goes on too long, meaning that for any given pattern, an ANN might start to consider the noise as part of the pattern.

Comparison of support vector machine and artificial neural network systems for drug/nondrug classification.

Support vector machine (SVM) and artificial neural network (ANN) systems were applied to a drug/nondrug classification problem as an example of binary decision problems in early-phase virtual compound filtering and screening.

The performance was compared using various different descriptor sets and descriptor combinations based on the 120 standard Ghose-Crippen fragment descriptors, a wide range of 180 different properties and physicochemical descriptors from the Molecular Operating Environment (MOE) package, and 225 topological pharmacophore (CATS) descriptors.

What are advantages of Artificial Neural Networks over Support Vector Machines? [closed]

The reason I ask is because it's easy to answer the opposite question: Support Vector Machines are often superior to ANNs because they avoid two major weaknesses of ANNs: (1) ANNs often converge on local minima rather than global minima, meaning that they are essentially 'missing the big picture' sometimes (or missing the forest for the trees) (2) ANNs often overfit if training goes on too long, meaning that for any given pattern, an ANN might start to consider the noise as part of the pattern.

What are advantages of Artificial Neural Networks over Support Vector Machines? [closed]

The reason I ask is because it's easy to answer the opposite question: Support Vector Machines are often superior to ANNs because they avoid two major weaknesses of ANNs: (1) ANNs often converge on local minima rather than global minima, meaning that they are essentially 'missing the big picture' sometimes (or missing the forest for the trees) (2) ANNs often overfit if training goes on too long, meaning that for any given pattern, an ANN might start to consider the noise as part of the pattern.

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