Methods

Algorithms and Methods

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General Methods

• back-propagation (Werbos, 1974)
• backward modeling
• batch training
  • 1. batch back-propagation
    2. training by epoch
• boosting (Schapire, 2001)
• cascade algorithm
• constructive training (of neural network)
• convex optimization
• covariance training
• direct error minimization
• discriminative training methods
• dynamic algorithms
• ensemble learning (Krogh and Vedelsby, 1995; Diettrich, 2000)
• explorative algorithms
• exploitive algorithms
• forward modeling
• genetic algorithms (Goldberg, 1989)
• global optimization techniques
• gradient methods
• greedy learning
• incremental training (of neural network)
  • 1. incremental back-propagation
    2. online training
    3. training by pattern
• kernel methods
• learning
  • 1. learning-by-example
• mini-batch training (Wilson and Martinez, 2003)
• off-line learning
• off-policy learning
• on-policy learning
• online learning
• policy iteration algorithm (Kaelbling et al., 1996)
• reinforcement learning
  • 1. episodic reinforcement learning
    2. model-based reinforcement learning
    3. model-free reinforcement learning
    4. non-episodic reinforcement learning
    5. off-policy learning
    6. on-policy learning
    7. tabular reinforcement learning
• supervised training
• temporal difference learning algorithms
• training
• unsupervised training
• value iteration algorithm
• wake-sleep algorithm
  • 1. contrastive wake-sleep
• weight update algorithm

Common Parameters of Algorithms

• fixed step-size
• dynamic step-size
• patience parameter
• momentum
• steepness parameter
• step size
  • 1. fixed step-size
    2. dynamic step-size
• variational bound

Named Algorithms

• Backprop
• Bayesian techniques (Neal, 1996)
• Cascade 2
  • 1. Cascade 2 with caching
• Cascade Correlation (Prechelt, 1997)
• Casper algorithm (Treadgold and Gedeon, 1997)
• Cerebellar Model Articulation Controller (CMAC) (Albus, 1975; Glanz et al., 1991; Sutton and Barto, 1998)
• Contrastive Divergence Learning
• Dyna-Q (Sutton and Barto, 1998)
• Explicit Explore or Exploit (Kearns and Singh, 1998)
• Gibbs Sampling
  • 1. Alternating Gibbs Sampling
• K-Nearest Neighbor
• Learning Vector Quantization (LVQ)
• Levenberg-Marquardt (More, 1977)
• Locality-Sensitive Hashing (LSH)
• Maximum Likelihood (ML) Learning
• Model-Based Interval Estimation (MBIE) (Strehl and Littman, 2004)
• Model-Based Policy Gradient methods (MBPG) (Wang and Dietterich, 2003)
• Monte Carlo Algorithms
• N-Step Return Algorithm
• Neural Fitted Q Iteration (Riedmiller, 2005)
  • 1. NFQ-SARSA(L)
• Optimal Brain Damage (LeCun et al., 1990)
• Orthogonal Least Squares (OLS)
• Particle Swarm (Kennedy and Eberhart, 1995)
• Prioritized Sweeping (Sutton and Barto, 1998)
• Q-Learning
  • 1. Delayed Q-learning (Strehl et al., 2006)
    2. Generalized Policy Iteration (GPI) (Sutton and Barto, 1998)
    3. Naive Q(λ) (Sutton and Barto, 1998)
    4. One-Step Q-Learning
    5. Peng’s Q(λ) (Peng and Williams, 1994)
    6. Q(λ)
    7. Watkin’s Q(λ) (Watkins, 1989)
• Q-SARSA(λ)
  • 1. One-Step Q-SARSA Algorithm
• Quickprop (Fahlman, 1988)
• R-Max (Brafman and Tennenholtz, 2002)
• RPROP (Riedmiller and Braun, 1993)
  • 1. iRPROP- (Igel and Hsken, 2000)
• SARSA(λ)
  • 1. On-Policy SARSA-Learning
    2. SARSA with Linear Function Approximators (Gordon, 2000)
• Simulated Annealing (Kirkpatrick et al., 1987)
• Sliding Window Cache
• SONN (Tenorio and Lee, 1989)
• Temporal Difference (TD) Learning
  • 1. Monte Carlo Prediction
    2. TD(0) Algorithm (Sutton, 1988)
    3. TD(λ) Algorithm
    4. TD(λ) Algorithm With Linear Function Approximators (Tsitsiklis and Roy, 1996)
    5. Temporal Difference (TD) Prediction Algorithm
• λ-Return Approach