376 research outputs found
Confusion-Based Online Learning and a Passive-Aggressive Scheme
International audienceThis paper provides the first ---to the best of our knowledge--- analysis of online learning algorithms for multiclass problems when the {\em confusion} matrix is taken as a performance measure. The work builds upon recent and elegant results on noncommutative concentration inequalities, i.e. concentration inequalities that apply to matrices, and, more precisely, to matrix martingales. We do establish generalization bounds for online learning algorithms and show how the theoretical study motivates the proposition of a new confusion-friendly learning procedure. This learning algorithm, called \copa (for COnfusion Passive-Aggressive) is a passive-aggressive learning algorithm; it is shown that the update equations for \copa can be computed analytically and, henceforth, there is no need to recourse to any optimization package to implement it
Detecting cyberattacks in industrial control systems using online learning algorithms
Industrial control systems are critical to the operation of industrial
facilities, especially for critical infrastructures, such as refineries, power
grids, and transportation systems. Similar to other information systems, a
significant threat to industrial control systems is the attack from
cyberspace---the offensive maneuvers launched by "anonymous" in the digital
world that target computer-based assets with the goal of compromising a
system's functions or probing for information. Owing to the importance of
industrial control systems, and the possibly devastating consequences of being
attacked, significant endeavors have been attempted to secure industrial
control systems from cyberattacks. Among them are intrusion detection systems
that serve as the first line of defense by monitoring and reporting potentially
malicious activities. Classical machine-learning-based intrusion detection
methods usually generate prediction models by learning modest-sized training
samples all at once. Such approach is not always applicable to industrial
control systems, as industrial control systems must process continuous control
commands with limited computational resources in a nonstop way. To satisfy such
requirements, we propose using online learning to learn prediction models from
the controlling data stream. We introduce several state-of-the-art online
learning algorithms categorically, and illustrate their efficacies on two
typically used testbeds---power system and gas pipeline. Further, we explore a
new cost-sensitive online learning algorithm to solve the class-imbalance
problem that is pervasive in industrial intrusion detection systems. Our
experimental results indicate that the proposed algorithm can achieve an
overall improvement in the detection rate of cyberattacks in industrial control
systems
From Cutting Planes Algorithms to Compression Schemes and Active Learning
Cutting-plane methods are well-studied localization(and optimization)
algorithms. We show that they provide a natural framework to perform
machinelearning ---and not just to solve optimization problems posed by
machinelearning--- in addition to their intended optimization use. In
particular, theyallow one to learn sparse classifiers and provide good
compression schemes.Moreover, we show that very little effort is required to
turn them intoeffective active learning methods. This last property provides a
generic way todesign a whole family of active learning algorithms from existing
passivemethods. We present numerical simulations testifying of the relevance
ofcutting-plane methods for passive and active learning tasks.Comment: IJCNN 2015, Jul 2015, Killarney, Ireland. 2015,
\<http://www.ijcnn.org/\&g
Active Nearest-Neighbor Learning in Metric Spaces
We propose a pool-based non-parametric active learning algorithm for general
metric spaces, called MArgin Regularized Metric Active Nearest Neighbor
(MARMANN), which outputs a nearest-neighbor classifier. We give prediction
error guarantees that depend on the noisy-margin properties of the input
sample, and are competitive with those obtained by previously proposed passive
learners. We prove that the label complexity of MARMANN is significantly lower
than that of any passive learner with similar error guarantees. MARMANN is
based on a generalized sample compression scheme, and a new label-efficient
active model-selection procedure
Soft confidence-weighted learning
Ministry of Education, Singapore under its Academic Research Funding Tier 1; Microsoft Research Gran
Deterministic Online Classification: Non-iteratively Reweighted Recursive Least-Squares for Binary Class Rebalancing
Deterministic solutions are becoming more critical for interpretability.
Weighted Least-Squares (WLS) has been widely used as a deterministic batch
solution with a specific weight design. In the online settings of WLS, exact
reweighting is necessary to converge to its batch settings. In order to comply
with its necessity, the iteratively reweighted least-squares algorithm is
mainly utilized with a linearly growing time complexity which is not attractive
for online learning. Due to the high and growing computational costs, an
efficient online formulation of reweighted least-squares is desired. We
introduce a new deterministic online classification algorithm of WLS with a
constant time complexity for binary class rebalancing. We demonstrate that our
proposed online formulation exactly converges to its batch formulation and
outperforms existing state-of-the-art stochastic online binary classification
algorithms in real-world data sets empirically
Open-Ended Learning of Visual and Multi-Modal Patterns
A common trend in machine learning and pattern classification research is the exploitation of massive amounts of information in order to achieve an increase in performance. In particular, learning from huge collections of data obtained from the web, and using multiple features generated from different sources, have led to significantly boost of performance on problems that have been considered very hard for several years. In this thesis, we present two ways of using these information to build learning systems with robust performance and some degrees of autonomy. These ways are Cue Integration and Cue Exploitation, and constitute the two building blocks of this thesis. In the first block, we introduce several algorithms to answer the research question on how to integrate optimally multiple features. We first present a simple online learning framework which is a wrapper algorithm based on the high-level integration approach in the cue integration literature. It can be implemented with existing online learning algorithms, and preserves the theoretical properties of the algorithms being used. We then extend the Multiple Kernel Learning (MKL) framework, where each feature is converted into a kernel and the system learns the cue integration classifier by solving a joint optimization problem. To make the problem practical, We have designed two new regularization functions making it possible to optimize the problem efficiently. This results in the first online method for MKL. We also show two algorithms to solve the batch problem of MKL. Both of them have a guaranteed convergence rate. These approaches achieve state-of-the-art performance on several standard benchmark datasets, and are order of magnitude faster than other MKL solvers. In the second block, We present two examples on how to exploit information between different sources, in order to reduce the effort of labeling a large amount of training data. The first example is an algorithm to learn from partially annotated data, where each data point is tagged with a few possible labels. We show that it is possible to train a face classification system from data gathered from Internet, without any human labeling, but generating in an automatic way possible lists of labels from the captions of the images. Another example is under the transfer learning setting. The system uses existing models from potentially correlated tasks as experts, and transfers their outputs over the new incoming samples, of a new learning task where very few labeled data are available, to boost the performance
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