#summary Artificial Intelligence Nouns

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==Common Entities==

  * action
   *# continuous action
  * action selection strategy
   *# confidence based exploration (Thrun, 1999)
   *# directed exploration
   *# eps.-greedy selection
   *# error-based directed exploration
   *# frequency-based directed exploration
   *# optimism in the face of uncertainty
   *# recency-based directed exploration (Sutton, 1990)
   *# tabu search (Abramson and Wechsler, 2003)
  * activation function
   *# hyperbolic tangent activation function
   *# linear activation function
   *# logistic function
   *# monotonic activation function
   *# normal sigmoid function
   *# periodic activation function
   *# sigmoid function
   *# symmetric sigmoid function
   *# symmetric sinus activation function
   *# threshold activation function
  * agent
   *# autonomous agent
  * artificial intelligence
  * back-propagation drawbacks
   *# local minima problem
   *# moving target problem
   *# step-size problem
  * belief nets
   *# directed belief nets
   *# sigmoid belief nets
  * binary codes
  * cause
  * cascade correlation architecture (Fahlman and Lebiere, 1990)
  * conditional random fields
  * connection
   *# autoregressive connections
   *# input connections
   *# lateral connection
   *# output connections
   *# short-cut connections
   *# symmetric connections
   *# temporal connections
   *# trainable connections
  * containment function
  * damping
  * dataset
   *# labeled data
   *# noise-free data
   *# sample
   *# sequential data
   *# test set
   *# training example
   *# training patterns
   *# training data-set
   *# unbiased example
   *# unlabeled data
   *# validation data-set
  * dimensionality reduction
   *# non-linear dimensionality reduction
  * discount rate
  * directed model
  * distributed representations
  * domain-specific kernel
  * dynamic programming
  * eligibility traces
    *# replacing eligibility traces
  * energy of joint configuration
  * environment
   *# stationary environment
  * epoch
  * error value
   *# mean square error (MSE)
  * experience value
   *# discounted future experience
   *# immediate experience value
  * experience value function
  * factorial distribution
  * feature
  * generative model
  * generalization
  * goal state
  * gradient
  * greedy strategy
  * inference
  * layer
   *# input layer
   *# hidden layer
   *# layer of features
   *# output layer
  * learning rate
  * likelihood
  * local optima (for neural network)
  * log likelihood
  * log probability
  * misclassification rate
  * neural networks
   *# [ArtificialNeuralNetwork artificial neural network (ANN)]
   *# cascading neural networks
   *# convolutional multilayer neural networks
   *# counterpropagation network
   *# deep neural networks
   *# feedforward networks
   *# fully connected neural network
   *# functional-link neural networks
   *# general regression neural network
   *# higher order networks
   *# multilayer feedforward artificial neural networks
   *# multilayer neural networks
   *# probabilistic neural network
   *# real-time recurrent learning networks
   *# recurrent backpropagation networks
   *# recurrent neural networks
  * neuron
   *# bias neuron
   *# binary neurons
   *# candidate neuron
   *# hidden neuron
   *# mean-field logistic unit
   *# output neuron
  * node (in the network)
   *# leaf node (in the network)
   *# unit
  * noise (in the data)
  * objective function
  * online inference
  * output
   *# actual output
   *# desired output
  * over-fitting
  * partial derivative
  * policy
   *# deterministic policy function
   *# optimal policy
   *# optimal deterministic policy
   *# stochastic policy function
  * posterior distribution
   *# aggregated posterior distribution
  * precision-recall curves
  * prior
   *# complementary prior
  * probability
  * probability density models
  * profit function
  * reward
   *# cumulative reward
   *# discounted future reward
   *# future reward
   *# immediate reward
   *# longterm reward
   *# short-term reward
  * reward value function
  * root mean squared error
  * second order statistics
  * selective attention approach
  * sensory input
  * shallow models
  * slackness of the bound
  * sloppy top-down specification
  * softmax function
  * state
   *# after-state
   *# continuous state
  * state-action space
  * stop function
  * structure (in the data)
  * training curve
  * value function
   *# action-value function
   *# state-value function
  * variable (for neural network)
   *# circular variables
   *# stochastic variable
  * weights
   *# frozen weights
   *# initial weights
   *# lateral weight

==Named Entities==

  * Adaline
  * ARTMAP Neural Networks
   *# Fuzzy ARTMAP
   *# Gaussian ARTMAP
  * Bellman Optimality Equation (Sutton and Barto, 1998)
  * Bernoulli Variables
  * Bidirectional Associative Memory (BAM)
  * Boltzmann Machine
   *# Conditional RBM model
   *# Restricted Boltzmann Machine (RBM)
   *# Semi-restricted Boltzmann Machines
   *# Temporal RBM
  * Boltzmann-Gibbs Selection
  * Deep Belief Nets
   *# Deep Autoencoders
  * Dynamic Bayes Nets
  * Elman Neural Networks
  * Finite Impulse Response (FIR) filter
  * Gaussian Processes
  * Gaussian Unit
  * Hebbian Theory
  * Hidden Markov Models (HMM)
  * Hopfield Net
  * Jordan Neural Network
  * Long Short-Term Memory (LSTM) Recurrent Network
  * Markov Decision Process (MDP)
  * Markov Environment
  * Markov Property
  * Markov State
  * Max-Boltzmann Selection
  * MNIST Test Set
  * MRF
   *# MRF-MBNN
  * Neocognitron
  * Perceptron
  * RBF Networks
  * Support Vector Machine (SVM)
  * T-step policy
  * T-step return
  * TF-IFD
  * Threshold Logical Units (TLU) Network
  * Time Delay Neural Network (TDNN)
  * UNI-SNE method