23,117 research outputs found
Boosting Classifiers for Drifting Concepts
This paper proposes a boosting-like method to train a classifier ensemble from data streams. It naturally adapts to concept drift and allows to quantify the drift in terms of its base learners. The algorithm is empirically shown to outperform learning algorithms that ignore concept drift. It performs no worse than advanced adaptive time window and example selection strategies that store all the data and are thus not suited for mining massive streams. --
Antifragility = Elasticity + Resilience + Machine Learning: Models and Algorithms for Open System Fidelity
We introduce a model of the fidelity of open systems - fidelity being
interpreted here as the compliance between corresponding figures of interest in
two separate but communicating domains. A special case of fidelity is given by
real-timeliness and synchrony, in which the figure of interest is the physical
and the system's notion of time. Our model covers two orthogonal aspects of
fidelity, the first one focusing on a system's steady state and the second one
capturing that system's dynamic and behavioural characteristics. We discuss how
the two aspects correspond respectively to elasticity and resilience and we
highlight each aspect's qualities and limitations. Finally we sketch the
elements of a new model coupling both of the first model's aspects and
complementing them with machine learning. Finally, a conjecture is put forward
that the new model may represent a first step towards compositional criteria
for antifragile systems.Comment: Preliminary version submitted to the 1st International Workshop "From
Dependable to Resilient, from Resilient to Antifragile Ambients and Systems"
(ANTIFRAGILE 2014), https://sites.google.com/site/resilience2antifragile
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
User response prediction, which models the user preference w.r.t. the
presented items, plays a key role in online services. With two-decade rapid
development, nowadays the cumulated user behavior sequences on mature Internet
service platforms have become extremely long since the user's first
registration. Each user not only has intrinsic tastes, but also keeps changing
her personal interests during lifetime. Hence, it is challenging to handle such
lifelong sequential modeling for each individual user. Existing methodologies
for sequential modeling are only capable of dealing with relatively recent user
behaviors, which leaves huge space for modeling long-term especially lifelong
sequential patterns to facilitate user modeling. Moreover, one user's behavior
may be accounted for various previous behaviors within her whole online
activity history, i.e., long-term dependency with multi-scale sequential
patterns. In order to tackle these challenges, in this paper, we propose a
Hierarchical Periodic Memory Network for lifelong sequential modeling with
personalized memorization of sequential patterns for each user. The model also
adopts a hierarchical and periodical updating mechanism to capture multi-scale
sequential patterns of user interests while supporting the evolving user
behavior logs. The experimental results over three large-scale real-world
datasets have demonstrated the advantages of our proposed model with
significant improvement in user response prediction performance against the
state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets:
https://github.com/alimamarankgroup/HPM
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
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