Title: "Just In Time Classifiers For Recurrent Concepts"

Abstract:
Many machine-learning techniques make the assumption that training and testing data are sampled from the same probability distribution. Unfortunately, in an increasing number of real-world learning scenarios data arrive in a stream, and the probabilistic properties of the data generating process might be changing with time, violating the above assumption.

Any algorithm or model that does not account for such change is almost certainly going to fail when data are sampled from a drifting or changing distribution, namely when data are affected by concept drift. Approaches for learning under concept drift can be divided in two main learning strategies: i) undergoing continuous adaptation to match the recent concept (passive approach), or ii) steadily monitoring the data stream to detect concept drift and eventually react (active approaches).  

In this talk I will present Just In Time (JIT) Classifiers, a family of classifiers that implement an active approach to handle concept drift. In particular, JIT classifiers monitor the data-generating process by means of change-detection tests, and exploits recurrent concepts by means of a practical representations of concepts that are built at runtime, and suitable operators that work on such representations.

Giacomo Boracchi


References
Cesare Alippi, Giacomo Boracchi and Manuel Roveri,
"Just In Time Classifiers for Recurrent Concepts",
IEEE Transactions on Neural Networks and Learning Systems, 2013. vol. 24, no. 4, pp. 620 - 634
doi:10.1109/TNNLS.2013.2239309 

Cesare Alippi, Giacomo Boracchi, Manuel Roveri,
"A just-in-time adaptive classification system based on the intersection of confidence intervals rule", 
Neural Networks, Elsevier vol. 24 (2011), pp. 791-800 
doi: 10.1016/j.neunet.2011.05.012 


BioSketch
Giacomo Boracchi received the M.S. degree in Mathematics from the Università Statale degli Studi di Milano, Italy, and the Ph.D. degree in Information Technology at Politecnico di Milano, Italy, in 2004 and 2008, respectively. He was researcher at Tampere International Center for Signal Processing, Finland, during 2004-2005. 

Currently, he is an assistant professor at the Dipartimento di Elettronica, Informazione e Bioingegneria of the Politecnico di Milano.
His main research interests encompass two different areas: computational intelligence and image analysis and enhancement; in particular, his research activity concerns learning methods for nonstationary environments, change/anomaly detection, computational imaging, and image restoration.