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This is a Template course for Meta-data XML
1.Maximum Entropy  (ME)
2.Boltzmann-Gibbs Distribution
3.Boltzmann-Gibbs (Cnt・d)
2.Boltzmann-Gibbs Distribution
3.Boltzmann-Gibbs (Cnt・d)
4.Exercise
5.Boltzmann-Gibbs (Cnt・d)
6.Boltzmann-Gibbs (Cnt・d)
7.Boltzmann-Gibbs (Cnt・d)
8.References
9.Maximum Entropy Approach
9.Maximum Entropy Approach
10.An Example
11.Formalism
12.Expected Values of f
13.Constraint Equation
14.Maximum Entropy Principle
15.Constrained Optimization Problem
16.Iterative Solution
17.Feature Selection
16.Iterative Solution
17.Feature Selection
18.Incremental Learning
19.Algorithm: Feature Selection
20.Approximation
19.Algorithm: Feature Selection
18.Incremental Learning
19.Algorithm: Feature Selection
20.Approximation
21.Approximation (cnt・d)
22.Approximate Solution
23.Conditional Random Field (CRF)
24.CRF
25.Feature Functions
26.Difference from MEMM
27.Difference from HMM
28.CRF Training Methods
29.Voted Perceptron
30.Voted Perceptron (cnt・d)
31.slide 31