Yu-Seop Kim

TL;DR: In this article, an approach to the topic extraction from text documents using probabilistic graphical models is presented, where multiple-cause networks with latent variables are used and the Helmholtz machines are utilized to ease the learning and inference.

TL;DR: A new methodology is proposed that selects target words after determining an appropriate collocation class by using a inter-word semantic similarity to resolve the sparseness problem and estimate the similarity by computing semantic distance of two synsets in Word-Net and term-to-term similarity in data-driven models.

TL;DR: A two-phase hybrid stock price forecasting model with cointegration tests and artificial neural networks using not only the related stocks to the target stock but also the past information as input features in neural networks showed an improved performance in forecasting than that of the usual neural networks.

TL;DR: Experimental results on KOSPI 200 show that a trading system based on the proposed framework outperforms the market average and makes appreciable profits and in view of risk management, the system is superior to a system trained by supervised learning.