Stochastic gradient boosting is a kind of the boosting methods invented by Jerome H.Friedman and it is known to be a very powerful method for making predictive models in some cases. In fact, FEG wins the second prize in KDD Cup 2009 by using this method. We survey the methodology of stochastic gradient boosting and introduce our analytical procedure in KDD Cup 2009. It is a good example where stochastic gradient boosting shows its effectiveness.
2 0 0 0 OA 半環に基づく前向き後ろ向きアルゴリズムの一般化
When we apply machine learning or data mining technique to sequential data, it is often required to take a summation over all the possible sequences. We cannot calculate such a summation directly from its definition in practice. Although the ordinary forward-backward algorithm provides an efficient way to do it, it is applicable to quite limited types of summations. In this paper, we propose general algebraic frameworks for generalization of the forward-backward algorithm. We show some examples falling within this framework and their importance.
2 0 0 0 OA テキストマイニングによる個人Blog データからの性格推定手法
In this paper, we propose egogram estimation method from weblog text data. Egogram is one of the personality models which illustrate the ego states of the users. In our method, the features which is appropriate for egogram are selected using the information gain of the each word which is contained in weblog text, and estimation is performed by Multinomial Naïve Bayes classifiers. We evaluate our method in some classification scenario and show its effectiveness.
2 0 0 0 OA 多様体学習と非線形次元縮約
We review algorithms and theory of manifold learning in machine learning.