Supervised Learning – Network is presented with the input and the desired output. – Uses a set of inputs for which the desired outputs results / classes are known.The difference between the desired and actual output is used to calculate adjustment to weights of the NN structure Unsupervised Learning – Network is not shown the desired output. – Concept is similar to clustering – It tries to create classification in the outcome.

Unsupervised Learning: – Only input stimuli (parameters) are presented to the network. The network is self organizing, that is, it organizes itself internally, so that each hidden processing elements and weights responds appropriately to a different set of input stimuli. – No knowledge is supplied about the classification of outputs. However, the number of categories into which the network classifies the inputs can be controlled by varying certain parameters in the model. In any case, human expert must examine the final classifications to assign a meaning & usefulness of results. Reinforcement Learning – In between Supervised & Unsupervised learning. – Network gets a feedback from the environment. 3

4 Supervised Learning – Recognizing hand-written digits, pattern recognition, regression. – Labeled examples(input, desired output) – Neural Network models: perceptron, feed-forward, radial basis function, support vector machine. Unsupervised Learning – Find similar groups of documents in the web, content addressable memory, clustering. – Unlabeled examples (different realizations of the input alone) – Neural Network models: self organizing maps, Hopfield networks.

Stability and plasticity is an important issue in competitive learning. How do we learn new things ( plasticity) and yet retain stability to ensure that the existing knowledge is not erased ( or corrupted). ART models are developed for this purpose. The network has sufficient supply of output units, but they are not used until deemed necessary. A unit is said to be committed, if it is being used. An input vector and the stored pattern are said to resonate when they are sufficiently closed similar. When the input vector is not sufficiently close to any of the existing prototype, a new category is created. 5

Two possible Class B Class A Class Red Class Green

It is based on a labeled training set. The class of each piece of data in training set is known. Class labels are pre- determined and provided in the training phase.

Task performed Classification Pattern Recognition NN model : Preceptron Feed-forward NN “What is the class of this data point?” Task performed Clustering NN Model : Self Organizing Maps “What groupings exist in this data?” “How is each data point related to the data set as a whole?”

There are a lot of other Unsupervised Learning Methods. Examples: – k-means – The EM Algorithm – Competitive Learning – Kohonen’s Neural Networks: Self-Organizing Maps – Principal Component Analysis, Autoassociation 9

Input : set of patterns P, from n-dimensional space S, but little/no information about their classification, evaluation, interesting features, etc. It must learn these by itself! : ) Tasks: – Clustering - Group patterns based on similarity – Vector Quantization - Fully divide up S into a small set of regions (defined by codebook vectors) that also helps cluster P. – Feature Extraction - Reduce dimensionality of S by removing unimportant features (i.e. those that do not help in clustering P)

Also defined – Self Organizing Map Learn a categorization of input space Neurons are connected into a 1-D or 2-D lattice. Each neuron represents a point in N-dimensional pattern space, defined by N weights During training, the neurons move around to try and fit to the data Changing the position of one neuron in data space influences the positions of its neighbors via the lattice connections

All inputs are connected by weights to each neuron size of neighbourhood changes as net learns Aim is to map similar inputs (sets of values) to similar neuron positions. Data is clustered because it is mapped to the same node or group of nodes

1. Initialization :Weights are set to unique random values 2. Sampling : Draw an input sample x and present in to network 3. Similarity Matching : The winning neuron i is the neuron with the weight vector that best matches the input vector i = argmin(j){ x – wj }

4. Updating : Adjust the weights of the winning neuron so that they better match the input. Also adjust the weights of the neighbouring neurons. ∆w j = η. h ij ( x – w j ) neighbourhood function : h ij over time neigbourhood function gets smaller Result: The neurons provide a good approximation of the input space and correspond

Input: training examples {x 1,…,x } without information about the hidden state. Clustering: goal is to find clusters of data sharing similar properties. Classifier Learning algorithm Classifier Learning algorithm (supervised) A broad class of unsupervised learning algorithms:

Classifier Goal is to minimize Learning algorithm

The ANN is given a set of patterns, P, from space, S, but little/no information about their classification, evaluation, interesting features, etc. It must learn these by itself! Tasks – Clustering - Group patterns based on similarity (Focus of this lecture) – Vector Quantization - Fully divide up S into a small set of regions (defined by codebook vectors) that also helps cluster P. – Probability Density Approximation - Find small set of points whose distribution matches that of P. – Feature Extraction - Reduce dimensionality of S by removing unimportant features (i.e. those that do not help in clustering P)

P = {(1 1 1), ( ), (1 -1 1)} = 3 patterns of length 3 i1i1 i2i2 i3i3 p1p1 p3p3 p2p2 wgt = 1/2 wgt = -1/2 InputsOutputs Given: input pattern I = (-1 1 1) Output (p 1 ) = -1/2 + 1/2 + 1/2 - 3/2 = -1 (Winner) Output (p 2 ) = 1/2 - 1/2 - 1/2 - 3/2 = -2 Output (p 3 ) = -1/2 - 1/2 + 1/2 - 3/2 = -2 wgt = -n/2 = -3/2 1

Classification learning uses a feedback mechanism An example is fed through the network using the existing weights The output value is O; the correct output value, i.e. the class in the example, is T (target) If O  T, some or all of the weights are changed slightly The extent of the change usually depends on T-O, called the error

A weight, w i, on a connection carrying signal, x i, can be modified by adding an amount  w i proportional to the error:  w i =  (T-O) x i where  is the learning rate  is a positive constant usually set at about 0.1 and gradually decreased during learning The update formula for w i is then w i  w i +  w i

The delta rule is often utilized by the most common class of ANNs called backpropagational neural networks. When a neural network is initially presented with a pattern it makes a random guess as to what it might be. It then sees how far its answer was from the actual one and makes an appropriate adjustment to its connection weights.

For each example in the training set: – the description attribute values are fed as input to the network – and propagated through to the output – each weight is updated This constitutes one epoch or cycle of learning The process is repeated till it is decided to stop Many thousands of epochs may be necessary The final set of weights represent the learned mapping

Conversion of attributes:

Use a single layer network (no hidden units) with step function to illustrate the delta rule Initialise weights as shown Set  = 0.1 Sunny Overcast Rain Temperature Low Normal High Windy w 0 =0.3 (bias) w 1 = -0.5 w 2 = -0.4 w 3 = 0.2 w 4 = 0.3 w 5 = 0.1 w 6 = -0.1 w 7 = -0.2 w 8 = 0.4 w1w1 w2w2 w3w3 w4w4 w5w5 w6w6 w7w7 w8w8

First example is: (sunny, 21, low, false) : yes encoded as: (1, 0, 0, 0.355, 1, 0, 0, 0) : 1 Calculate weighted sum and threshold at 0 (because bias unit is included): Sum = -1* *1 -0.4* * * *1 -0.1*0 -0.2*0 +0.4*0 = < 0  O = 0 T = 1 thus error is T - O = = 1 Using delta rule change weights by:  w i =  (T-O) x i = 0.1 * 1 * x i = 0.1 * x i

The Self Organising Map or Kohonen network uses unsupervised learning. Kohonen networks have a single layer of units and, during training, clusters of units become associated with different classes (with statistically similar properties) that are present in the training data. The Kohonen network is useful in clustering applications.

28 Clustering Hierarchical Clustering E E1 E2 E3 E4 E7 E8 E E1 E2 E7 E8

Each element in a training set is paired with an acceptable response Network makes successive passes through the examples The weights adjust toward the goal state. 29