LEARNING IN THE REAL-WORLD

In this paper we define and characterize the process of developing a ' 'real-world'' Machine Learning application, with its difficulties and relevant issues, distinguishing it from the popular practice of exploi ting ready-to-use data sets. To this aim, we analyze and summarize the lessons learned from applying Machine Learning techniques to a variet y of problems. We believe that these lessons, though primarily based o n our personal experience, can be generalized to a wider range of situ ations and are supported by the reported experiences of other research ers.