Question

Which of the following are true when comparing ANNs and SVMs?
A) ANN error surface has multiple local minima while SVM error surface has only one minima
B) After training, an ANN might land on a different minimum each time, when initialized with random weights during each run.
C) In training, ANN's error surface is navigated using a gradient descent technique while SVM's error surface is navigated using convex optimization solvers.
D) As shown for Perceptron, there are some classes of functions that cannot be learnt by an ANN. An SVM can learn a hyperplane for any kind of distribution.

Answer 1

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 results indicate that solutions obtained by SVM training seem to be more robust with a smaller standard error compared to ANN training. Generally, the SVM classifier yielded slightly higher prediction accuracy than ANN, irrespective of the type of descriptors used for molecule encoding, the size of the training data sets, and the algorithm employed for neural network training. 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. For the complete set of 525 descriptors cross-validated classification by SVM yielded 82% correct predictions (Matthews cc = 0.63), whereas ANN reached 80% correct predictions (Matthews cc = 0.58). Although SVM outperformed the ANN classifiers with regard to overall prediction accuracy, both methods were shown to complement each other, as the sets of true positives, false positives (overprediction), true negatives, and false negatives (underprediction) produced by the two classifiers were not identical. The theory of SVM and ANN training is briefly reviewed.

Answer 2

ANN error surface has multiple local minima while the SVM error surface has only one minimum: True.

After training, an ANN might land on a different minimum each time, when initialized with random weights during each run: True

In training, ANN's error surface is navigated using a gradient descent technique while SVM's error surface is navigated using convex optimization solvers: True

Option (A) (B) (C)

Explanation:

• SVM stands for 'Support Vector Machines' and ANN elaborates 'Artificial Neural Network' and both are the elements of machine intelligence i.e. artificial intelligence.
• SVM is a machine learning algorithm that is utilised for both regression and classification challenges. ANN are computing systems that are encouraged by, but not similar to, biological neural networks which establish animal brains.

Learn more about SVM

After training an SVM, we can discard all examples which do not support vectors and can still classify new examples?

A) TRUE

B) FALSE

https://brainly.in/question/5893833

Which model helps svm to implement the algorithm in high dimensional space?

https://brainly.in/question/11169866