2. Theory Simulations Applications Theory Simulations Applications

3. Analytic techniques modeled after the processes of learning in the cognitive system and the neurological functions of the brainAnalytic techniques modeled after the processes of learning in the cognitive system and the neurological functions of the brain Capable of predicting new observations from other observationsCapable of predicting new observations from other observations Analytic techniques modeled after the processes of learning in the cognitive system and the neurological functions of the brainAnalytic techniques modeled after the processes of learning in the cognitive system and the neurological functions of the brain Capable of predicting new observations from other observationsCapable of predicting new observations from other observations

4. Information is received at the synapses on its dendritesInformation is received at the synapses on its dendrites An electro-chemical transmission occurs at the synapsesAn electro-chemical transmission occurs at the synapses At the cell body, a summation of the electric impulses takes placeAt the cell body, a summation of the electric impulses takes place If summation meets a particular threshold, the neuron will send a signalIf summation meets a particular threshold, the neuron will send a signal Information is received at the synapses on its dendritesInformation is received at the synapses on its dendrites An electro-chemical transmission occurs at the synapsesAn electro-chemical transmission occurs at the synapses At the cell body, a summation of the electric impulses takes placeAt the cell body, a summation of the electric impulses takes place If summation meets a particular threshold, the neuron will send a signalIf summation meets a particular threshold, the neuron will send a signal

6. Receives a number of inputsReceives a number of inputs Each input has a weight that corresponds to synaptic efficacy in a biological neuronEach input has a weight that corresponds to synaptic efficacy in a biological neuron Weighted sum of the inputs is formedWeighted sum of the inputs is formed Activation if weighted sum meets the threshold valueActivation if weighted sum meets the threshold value Receives a number of inputsReceives a number of inputs Each input has a weight that corresponds to synaptic efficacy in a biological neuronEach input has a weight that corresponds to synaptic efficacy in a biological neuron Weighted sum of the inputs is formedWeighted sum of the inputs is formed Activation if weighted sum meets the threshold valueActivation if weighted sum meets the threshold value

8. Extensively used for classification and regression problems Does not suffer the curse of dimensionality Valuable for solving problems with a large number of inputs Extensively used for classification and regression problems Does not suffer the curse of dimensionality Valuable for solving problems with a large number of inputs

9. Maximize the margin between the separating patterns using a hyperplaneMaximize the margin between the separating patterns using a hyperplane Margin of separation is the separation between the hyperplane and the closest data pointMargin of separation is the separation between the hyperplane and the closest data point The goal is to find the optimal hyperplane to maximize the margin of separationThe goal is to find the optimal hyperplane to maximize the margin of separation Maximize the margin between the separating patterns using a hyperplaneMaximize the margin between the separating patterns using a hyperplane Margin of separation is the separation between the hyperplane and the closest data pointMargin of separation is the separation between the hyperplane and the closest data point The goal is to find the optimal hyperplane to maximize the margin of separationThe goal is to find the optimal hyperplane to maximize the margin of separation

11. Support vectors are data points that lie directly on the decision boundary Support vectors serve a very important role in the operation of this algorithm Support vectors are data points that lie directly on the decision boundary Support vectors serve a very important role in the operation of this algorithm

13. Slack variables consider the case of nonseparable patterns, and measure the deviation of the data points from the boundary of the region of separability

15. Given the training example: Find the optimum values of the weight vector and bias such that they satisfy the constraint: Given the training example: Find the optimum values of the weight vector and bias such that they satisfy the constraint:

16. Minimize the cost functional: The parameter C quantifies the trade-off between training error and system capacity Minimize the cost functional: The parameter C quantifies the trade-off between training error and system capacity

17. The inner-product kernel may be used to construct the optimal hyperplane in the feature space without having to consider the feature space itself in explicit form The requirement on the construction of the kernel is that it satisfies Mercel’s theorem The inner-product kernel may be used to construct the optimal hyperplane in the feature space without having to consider the feature space itself in explicit form The requirement on the construction of the kernel is that it satisfies Mercel’s theorem

18. polynomial learning machine: radial-basis function network kernel: two-layer perceptron kernel: polynomial learning machine: radial-basis function network kernel: two-layer perceptron kernel:

19. Linearly Separable Data Nonlinearly Separable Data Polynomial Mapping Classification Example Linearly Separable Data Nonlinearly Separable Data Polynomial Mapping Classification Example

24. The goal is to classify the class of an iris given two features - pedal length and pedal width

29. Nonlinear Equalization Text Categorization 3D Object Recognition Nonlinear Equalization Text Categorization 3D Object Recognition

30. Output of a channel is used as the input of the classifier Channel output is transformed into a pattern space, which is mapped into a higher-dimensional feature space Classifier matches a delayed version of the original signal Output of a channel is used as the input of the classifier Channel output is transformed into a pattern space, which is mapped into a higher-dimensional feature space Classifier matches a delayed version of the original signal

31. Utilizing Information Retrieval Theory, word stems are used as representation units Each distinct word corresponds to a feature, with the frequency of occurrence as its value To avoid an unnecessarily large number of features, only words of a threshold frequency are considered, and “stop-words” (“and”, “or”, etc.) are ignored Utilizing Information Retrieval Theory, word stems are used as representation units Each distinct word corresponds to a feature, with the frequency of occurrence as its value To avoid an unnecessarily large number of features, only words of a threshold frequency are considered, and “stop-words” (“and”, “or”, etc.) are ignored

32. Many views of an object are given to the SVM Types of features selected: –Shape of the object –Color of the object –Shape and the color of the object Many views of an object are given to the SVM Types of features selected: –Shape of the object –Color of the object –Shape and the color of the object

33. Support vector machines method is an efficient, robust method for classification Advantages: –Small number of adjustable parameters –Doesn’t require prior information or heuristic assumptions –Train with relatively small amounts of data Support vector machines method is an efficient, robust method for classification Advantages: –Small number of adjustable parameters –Doesn’t require prior information or heuristic assumptions –Train with relatively small amounts of data