In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks, most commonly applied to analyzing visual imagery.
A CNN consists of an input and an output layer, as well as multiple hidden layers. The hidden layers of a CNN typically consist of convolutional layers, pooling layers, fully connected layers and normalization layers.
Convolutional layers apply a convolution operation to the input, passing the result to the next layer. The convolution emulates the response of an individual neuron to visual stimuli.
Based on the hand-writing data set for supervised learning SVM can be a good choice for training the model.
The MLP consists of three or more layers (an input and an output layer with one or more hidden layers) of nonlinearly-activating nodes. Since MLPs are fully connected, each node in one layer connects with a certain weight to every node in the following layer.
K-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.
The KNN is the fundamental and simplest classification technique when there is little or no prior knowledge about the distribution of the data. This rule simply retains the entire training set during learning and assigns to each query a class represented by the majority label of its k-nearest neighbors in the training set. The Nearest Neighbor rule (NN) is the simplest form of KNN when K = 1.
A Random Forest is a classifier consisting of collection of tree-structured classifiers where independent random vectors are distributed identically and each tree cast a unit vote for the most popular class at input x. A random vector is generated which is independent of the past random vectors with same distribution and a tree is generated by using the training test.
The KNN is the fundamental and simplest classification technique when there is little or no prior knowledge about the distribution of the data. This rule simply retains the entire training set during learning and assigns to each query a class represented by the majority label of its k-nearest neighbors in the training set. The Nearest Neighbor rule (NN) is the simplest form of KNN when K = 1.
