The University of Osaka · Computer Science
Professor Joe Suzuki's research lab specializes in statistical machine learning, with a focus on Bayesian networks, model selection, and information-theoretic principles such as the Minimum Description Length (MDL) and Bayesian estimation. The lab develops theoretically grounded algorithms for learning probabilistic graphical models, particularly for both discrete and continuous variables, emphasizing consistency, efficiency, and robustness in data-driven inference. A key direction involves constructing nonparametric estimators for mutual information and conditional probabilities without assuming parametric forms, enabling applications to diverse data types.
Figures are computed from collected data and may differ slightly.
This paper addresses a Markov chain analysis of genetic algorithms (GAs), in particular for a variety called a modified elitist strategy. The modified elitist strategy generates the current population of M individuals by reserving the individual with the highest fitness value from the previous generation and generating M-1 individuals through a generation change. The author's analysis is based on a Markov chain: by assuming a simple GA in which the genetic operation in the generation changes is
In this paper, the problem of learning a Bayesian belief network (BBN) from given examples based on the minimum description length (MDL) principle is addressed. Given examples, the learning algorithm based on the MDL principle computes for each network the total of description length of the network and that of the examples given the network, and finds a network with the minimum value. We provide a search algorithm that reduces the computation time and at the same time is sure to find the network
This paper addresses the problem of learning Bayesian belief networks (BBN) based on the minimum description length (MDL) principle. First, we give a formula of description length based on which the MDL-based procedure learns a BBN. Secondly, we point out that the difference between the MDL-based and Cooper and Herskovits procedures is essentially in the priors rather than in the approaches (MDL and Bayesian), and recommend a class of priors from which the formula is obtained. Finally, we show a
This paper considers model selection in classification. In many applications such as pattern recognition, probabilistic inference using a Bayesian network, prediction of the next in a sequence based on a Markov chain, the conditional probability P(Y=y|X=x) of class yisinY given attribute value xisinX is utilized. By model we mean the equivalence relation in X: for x,x'isinXx~x'hArrP(Y=y|X=x)=P(Y=y|X=x'), forall yisinY. By classification we mean the number of such equivalence classes is finite. W
Given data, not knowing the distribution, we wish to construct a forest (Markov graph) relative to which the description length is minimized, connecting edges with larger estimated mutual information of each pair of random variables step by step (the Chow-Liu algorithm) to balance simplicity of the forest and fitness of the data, where the random variables are not to be either discrete or continuous. To this end, we construct a Bayesian measure over the data sequences, and propose the Bayesian e
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