Goebel et al. [4] presented a unified decomposition of ensemble loss for explaining ensemble performance. They considered democratic voting schemes with uniform weights, where the various base classifiers each can vote for a single class once only. In this article, we generalize their decomposition to cover weighted, probabilistic voting schemes and non-uniform (progressive) voting schemes. Emp...

We consider how to make probability forecasts of binary labels. Our main mathematical result is that for any continuous gambling strategy used for detecting disagreement between the forecasts and the actual labels, there exists a forecasting strategy whose forecasts are ideal as far as this gambling strategy is concerned. A forecasting strategy obtained in this way from a gambling strategy demo...

Prediction markets provide an efficient means to assess uncertain quantities from forecasters. Traditional and competitive strictly proper scoring rules have been shown to incentivize players to provide truthful probabilistic forecasts. However, we show that when those players can cooperate, these mechanisms can instead discourage them from reporting what they really believe. When players with ...

This chapter covers different approaches that may be taken when building an ensemble method, through studying specific examples of each approach from research conductded by the author. A method called Negative Correlation Learning illustrates a decision level combination approach with individual calssifiers trained co-operatively. The Model level combination paradigm is illustrated via a tree c...

The paper introduces a modified version of the classical Coupon Collector’s Problem entailing exchanges and cooperation between multiple players. Results of the development show that, within a proper Markov framework, the complexity of the Cooperative Multiplayer Coupon Collectors’ Problem can be attacked with an eye to the modeling of social strategies and community behaviors. The cost of coop...

In recent years, authors have focused on modeling and forecasting volatility in financial series it is crucial for the characterization of markets, portfolio optimization and asset valuation. One of the most used methods to forecast market volatility is the linear regression. Nonetheless, the errors in prediction using this approach are often quite high. Hence, continued research is conducted t...

in this paper semi-markov models are used to forecast the triple dimensions of next earthquake occurrences. each earthquake can be investigated in three dimensions including temporal, spatial and magnitude. semi-markov models can be used for earthquake forecasting in each arbitrary area and each area can be divided into several zones. in semi-markov models each zone can be considered as a state...