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  1. Artificial neural network
  2. Connection weight
  3. Transformation transfer function
  4. ANN
  5. Perceptron
  6. Axon
  7. Threshold value
  8. Neural network
  9. Neuron
  10. Hidden layer
  11. Processing elements
  12. Supervised learning
  13. Summation function
  14. Parallel processing
  15. Pattern recognition
  16. Backpropagation
  17. K-nearest neighbor
  18. Sigmoid (logical activation) function
  19. Neural computing
  20. Nucleus
  21. Dendrites
  22. Kohonen's self organizing feature map
  1. a Central processing portion of a neuron
  2. b A type of neural network model for machine learning
  3. c The weight associated with each link in a neural network model. Assessed by neural networks learning algorithms.
  4. d A prediction method for classification as well as regression type prediction problems where the prediction is made based on the similarity to K neighbors.
  5. e AKA "artificial neural network"
  6. f A technique of matching an external pattern to a pattern stored in a computer's memory (ie the process of classifying data into predetermined categories). Pattern recognition is used in inference engine, image processing, neural computing and speech recognition.
  7. g A hurdle value for the output of a neuron to trigger the next level of neurons. Is an output value is smaller than a threshold value, it will not be passed to the next level of neurons.
  8. h A cell (ie, processing element) of a biological or artificial neural network.
  9. i The best-known learning algorithm and neural computing where the learning is done by comparing computed output to desired output of training cases
  10. j An S-shaped transfer function in the range of 0 to 1.
  11. k In a neural network, the function that sums and transforms input before a neuron fires. It shows the relationship between the internal activation level and the output of a neuron.
  12. l The middle layer of an artificial neural network that has three or more layers.
  13. m The part of a biological neuron that provides inputs to the cell
  14. n Artificial Neural Network
  15. o An outgoing connection (ie, terminal) from a biological neuron.
  16. p A neuron and a neural network.
  17. q A mechanism to add all the inputs coming into a particular neuron.
  18. r A method of training artificial neural networks in which sample cases are shown to the network as input and the weights are adjusted to minimize the error in the output.
  19. s Computer technology that attempts to build computers that operate like a human brain. The machines possess simultaneous memory storage and work with ambiguous information. Sometimes called a neural network.
  20. t An advanced computer processing technique that allows a computer to perform multiple processes at once, in parallel.
  21. u An early neural network structure that uses no hidden layer.
  22. v An experimental computer design aimed at intelligent computers that operate in a manner modeled on the functioning of the human brain.