Table of Contents
What is the difference between SVM and ANN?
The difference is mainly on how non-linear data is classified. Basically, SVM utilizes nonlinear mapping to make the data linear separable, hence the kernel function is the key. However, ANN employs multi-layer connection and various activation functions to deal with nonlinear problems.
What kind of classifier is an Hmm?
A Hidden Markov Model (HMM) is a sequence classifier. As other machine learning algorithms it can be trained, i.e.: given labeled sequences of observations, and then using the learned parameters to assign a sequence of labels given a sequence of observations.
How is SVM different from Perceptron?
Perceptron stops after it classifies data correctly whereas SVM stops after finding the best plane that has the maximum margin, i.e. the maximum distance between data points of both classes. Maximizing the margin distance provides some reinforcement so that future data points can be classified with more confidence.
What is SVM in simple words?
SVM or Support Vector Machine is a linear model for classification and regression problems. It can solve linear and non-linear problems and work well for many practical problems. The idea of SVM is simple: The algorithm creates a line or a hyperplane which separates the data into classes.
Which is better logistics or SVM?
SVM try to maximize the margin between the closest support vectors whereas logistic regression maximize the posterior class probability. SVM is deterministic (but we can use Platts model for probability score) while LR is probabilistic. For the kernel space, SVM is faster.
What is the main difference between a perceptron classifier and an SVM classifier?
The major practical difference between a (kernel) perceptron and SVM is that perceptrons can be trained online (i.e. their weights can be updated as new examples arrive one at a time) whereas SVMs cannot be. See this question for information on whether SVMs can be trained online.
What is a SVM kernel?
A kernel is a function used in SVM for helping to solve problems. They provide shortcuts to avoid complex calculations. The amazing thing about kernel is that we can go to higher dimensions and perform smooth calculations with the help of it. We can go up to an infinite number of dimensions using kernels.