AAAl 2014 Tutorial Latent tree models Part Il: Learning Algorithms Nevin L Zhang Dept. of computer Science Engineering The hong Kong Univ of Sci. Tech http://www.cse.ust.hk/lzhang
Latent Tree Models Part III: Learning Algorithms Nevin L. Zhang Dept. of Computer Science & Engineering The Hong Kong Univ. of Sci. & Tech. http://www.cse.ust.hk/~lzhang AAAI 2014 Tutorial
Learning latent Tree Models X1)(X2)(X3 X5(X6)(X7 To Determine 1. Number of latent variables 2. Cardinality of each latent variable 3. Model structure 4. Probability distributions Model selection :1.2.3 Parameter estimation 4 AAAl2014 Tutorial Nevin L Zhang HKUST
AAAI 2014 Tutorial Nevin L. Zhang HKUST 2 To Determine 1. Number of latent variables 2. Cardinality of each latent variable 3. Model structure 4. Probability distributions Learning Latent Tree Models Model selection: 1, 2, 3 Parameter estimation: 4
Light Bulb lustration Run interactive program"LightBulbllustration jar' Ilustrate the possibility of inferring latent variables and latent structures from observed co-occurrence patterns 6900 「上一步[下一步一「结果 「暂停加速 start next result faster slower
AAAI 2014 Tutorial Nevin L. Zhang HKUST 3 Run interactive program “LightBulbIllustration.jar” Illustrate the possibility of inferring latent variables and latent structures from observed co-occurrence patterns. Light Bulb Illustration
Part lll: Learning algorithms Introduction Search-based algorithms Algorithms based on variable clustering Distance-based algorithms Empirical comparisons Spectral methods for parameter estimation AAAl2014 Tutorial Nevin L Zhang HKUST
AAAI 2014 Tutorial Nevin L. Zhang HKUST 4 Part III: Learning Algorithms Introduction Search-based algorithms Algorithms based on variable clustering Distance-based algorithms Empirical comparisons Spectral methods for parameter estimation
Search Algorithms a search algorithm explores the space of regular models guided by a scoring function Start with an initial model s terate until model score ceases to increase Modify the current model in various ways to generate a list of candidate models Evaluate the candidate models using the scoring function Pick the best candidate model What scoring function to use? How do we evaluate candidate models? This is the model selection problem AAAl2014 Tutorial Nevin L Zhang HKUST 5
AAAI 2014 Tutorial Nevin L. Zhang HKUST 5 A search algorithm explores the space of regular models guided by a scoring function: Start with an initial model Iterate until model score ceases to increase Modify the current model in various ways to generate a list of candidate models. Evaluate the candidate models using the scoring function. Pick the best candidate model What scoring function to use? How do we evaluate candidate models? This is the model selection problem. Search Algorithms