An Entropy-based Learning Algorithm of Bayesian Conditional Trees
This is an incremental improvement for digit recognition tasks, addressing specific challenges in distinguishing similar digits.
The paper tackles the problem of handwritten digit recognition by modifying Chow and Liu's algorithm to group hard-to-distinguish digits into classes and build optimal conditional trees per class, rather than per digit, though no concrete performance numbers are provided.
This article offers a modification of Chow and Liu's learning algorithm in the context of handwritten digit recognition. The modified algorithm directs the user to group digits into several classes consisting of digits that are hard to distinguish and then constructing an optimal conditional tree representation for each class of digits instead of for each single digit as done by Chow and Liu (1968). Advantages and extensions of the new method are discussed. Related works of Wong and Wang (1977) and Wong and Poon (1989) which offer a different entropy-based learning algorithm are shown to rest on inappropriate assumptions.