17 research outputs found
A Novel Progressive Multi-label Classifier for Classincremental Data
In this paper, a progressive learning algorithm for multi-label
classification to learn new labels while retaining the knowledge of previous
labels is designed. New output neurons corresponding to new labels are added
and the neural network connections and parameters are automatically
restructured as if the label has been introduced from the beginning. This work
is the first of the kind in multi-label classifier for class-incremental
learning. It is useful for real-world applications such as robotics where
streaming data are available and the number of labels is often unknown. Based
on the Extreme Learning Machine framework, a novel universal classifier with
plug and play capabilities for progressive multi-label classification is
developed. Experimental results on various benchmark synthetic and real
datasets validate the efficiency and effectiveness of our proposed algorithm.Comment: 5 pages, 3 figures, 4 table
Distributed Online Big Data Classification Using Context Information
Distributed, online data mining systems have emerged as a result of
applications requiring analysis of large amounts of correlated and
high-dimensional data produced by multiple distributed data sources. We propose
a distributed online data classification framework where data is gathered by
distributed data sources and processed by a heterogeneous set of distributed
learners which learn online, at run-time, how to classify the different data
streams either by using their locally available classification functions or by
helping each other by classifying each other's data. Importantly, since the
data is gathered at different locations, sending the data to another learner to
process incurs additional costs such as delays, and hence this will be only
beneficial if the benefits obtained from a better classification will exceed
the costs. We model the problem of joint classification by the distributed and
heterogeneous learners from multiple data sources as a distributed contextual
bandit problem where each data is characterized by a specific context. We
develop a distributed online learning algorithm for which we can prove
sublinear regret. Compared to prior work in distributed online data mining, our
work is the first to provide analytic regret results characterizing the
performance of the proposed algorithm
Online Learning with an Almost Perfect Expert
We study the multiclass online learning problem where a forecaster makes a
sequence of predictions using the advice of experts. Our main contribution
is to analyze the regime where the best expert makes at most mistakes and
to show that when , the expected number of mistakes made by
the optimal forecaster is at most . We also describe
an adversary strategy showing that this bound is tight and that the worst case
is attained for binary prediction
Distributed Online Learning via Cooperative Contextual Bandits
In this paper we propose a novel framework for decentralized, online learning
by many learners. At each moment of time, an instance characterized by a
certain context may arrive to each learner; based on the context, the learner
can select one of its own actions (which gives a reward and provides
information) or request assistance from another learner. In the latter case,
the requester pays a cost and receives the reward but the provider learns the
information. In our framework, learners are modeled as cooperative contextual
bandits. Each learner seeks to maximize the expected reward from its arrivals,
which involves trading off the reward received from its own actions, the
information learned from its own actions, the reward received from the actions
requested of others and the cost paid for these actions - taking into account
what it has learned about the value of assistance from each other learner. We
develop distributed online learning algorithms and provide analytic bounds to
compare the efficiency of these with algorithms with the complete knowledge
(oracle) benchmark (in which the expected reward of every action in every
context is known by every learner). Our estimates show that regret - the loss
incurred by the algorithm - is sublinear in time. Our theoretical framework can
be used in many practical applications including Big Data mining, event
detection in surveillance sensor networks and distributed online recommendation
systems
Online Clustering of Bandits
We introduce a novel algorithmic approach to content recommendation based on
adaptive clustering of exploration-exploitation ("bandit") strategies. We
provide a sharp regret analysis of this algorithm in a standard stochastic
noise setting, demonstrate its scalability properties, and prove its
effectiveness on a number of artificial and real-world datasets. Our
experiments show a significant increase in prediction performance over
state-of-the-art methods for bandit problems.Comment: In E. Xing and T. Jebara (Eds.), Proceedings of 31st International
Conference on Machine Learning, Journal of Machine Learning Research Workshop
and Conference Proceedings, Vol.32 (JMLR W&CP-32), Beijing, China, Jun.
21-26, 2014 (ICML 2014), Submitted by Shuai Li
(https://sites.google.com/site/shuailidotsli