Learning Active Learning from Data

In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate sample in a particular learning state. By formulating the query selection procedure as a regression problem we are not restricted to working with existing AL heuristics; instead, we learn strategies based on experience from previous AL outcomes. We show that a strategy can be learnt either from simple synthetic 2D datasets or from a subset of domain-specific data. Our method yields strategies that work well on real data from a wide range of domains

Financial Computational Intelligence

Artificial intelligence decision support system is always a popular topic in providing the human with an optimized decision recommendation when operating under uncertainty in complex environments. The particular focus of our discussion is to compare different methods of artificial intelligence decision support systems in the investment domain – the goal of investment decision-making is to select an optimal portfolio that satisfies the investor’s objective, or, in other words, to maximize the investment returns under the constraints given by investors. In this study we apply several artificial intelligence systems like Influence Diagram (a special type of Bayesian network), Decision Tree and Neural Network to get experimental comparison analysis to help users to intelligently select the best portfoliArtificial intelligence, neural network, decision tree, bayesian network