55,232 research outputs found
Efficient Online Decision Tree Learning with Active Feature Acquisition
Constructing decision trees online is a classical machine learning problem.
Existing works often assume that features are readily available for each
incoming data point. However, in many real world applications, both feature
values and the labels are unknown a priori and can only be obtained at a cost.
For example, in medical diagnosis, doctors have to choose which tests to
perform (i.e., making costly feature queries) on a patient in order to make a
diagnosis decision (i.e., predicting labels). We provide a fresh perspective to
tackle this practical challenge. Our framework consists of an active planning
oracle embedded in an online learning scheme for which we investigate several
information acquisition functions. Specifically, we employ a surrogate
information acquisition function based on adaptive submodularity to actively
query feature values with a minimal cost, while using a posterior sampling
scheme to maintain a low regret for online prediction. We demonstrate the
efficiency and effectiveness of our framework via extensive experiments on
various real-world datasets. Our framework also naturally adapts to the
challenging setting of online learning with concept drift and is shown to be
competitive with baseline models while being more flexible
Adaptive Endpointing with Deep Contextual Multi-armed Bandits
Current endpointing (EP) solutions learn in a supervised framework, which
does not allow the model to incorporate feedback and improve in an online
setting. Also, it is a common practice to utilize costly grid-search to find
the best configuration for an endpointing model. In this paper, we aim to
provide a solution for adaptive endpointing by proposing an efficient method
for choosing an optimal endpointing configuration given utterance-level audio
features in an online setting, while avoiding hyperparameter grid-search. Our
method does not require ground truth labels, and only uses online learning from
reward signals without requiring annotated labels. Specifically, we propose a
deep contextual multi-armed bandit-based approach, which combines the
representational power of neural networks with the action exploration behavior
of Thompson modeling algorithms. We compare our approach to several baselines,
and show that our deep bandit models also succeed in reducing early cutoff
errors while maintaining low latency
Multi-Label Noise Robust Collaborative Learning Model for Remote Sensing Image Classification
The development of accurate methods for multi-label classification (MLC) of
remote sensing (RS) images is one of the most important research topics in RS.
Methods based on Deep Convolutional Neural Networks (CNNs) have shown strong
performance gains in RS MLC problems. However, CNN-based methods usually
require a high number of reliable training images annotated by multiple
land-cover class labels. Collecting such data is time-consuming and costly. To
address this problem, the publicly available thematic products, which can
include noisy labels, can be used to annotate RS images with zero-labeling
cost. However, multi-label noise (which can be associated with wrong and
missing label annotations) can distort the learning process of the MLC
algorithm. The detection and correction of label noise are challenging tasks,
especially in a multi-label scenario, where each image can be associated with
more than one label. To address this problem, we propose a novel noise robust
collaborative multi-label learning (RCML) method to alleviate the adverse
effects of multi-label noise during the training phase of the CNN model. RCML
identifies, ranks and excludes noisy multi-labels in RS images based on three
main modules: 1) discrepancy module; 2) group lasso module; and 3) swap module.
The discrepancy module ensures that the two networks learn diverse features,
while producing the same predictions. The task of the group lasso module is to
detect the potentially noisy labels assigned to the multi-labeled training
images, while the swap module task is devoted to exchanging the ranking
information between two networks. Unlike existing methods that make assumptions
about the noise distribution, our proposed RCML does not make any prior
assumption about the type of noise in the training set. Our code is publicly
available online: http://www.noisy-labels-in-rs.orgComment: Our code is publicly available online:
http://www.noisy-labels-in-rs.or
RELEAF: An Algorithm for Learning and Exploiting Relevance
Recommender systems, medical diagnosis, network security, etc., require
on-going learning and decision-making in real time. These -- and many others --
represent perfect examples of the opportunities and difficulties presented by
Big Data: the available information often arrives from a variety of sources and
has diverse features so that learning from all the sources may be valuable but
integrating what is learned is subject to the curse of dimensionality. This
paper develops and analyzes algorithms that allow efficient learning and
decision-making while avoiding the curse of dimensionality. We formalize the
information available to the learner/decision-maker at a particular time as a
context vector which the learner should consider when taking actions. In
general the context vector is very high dimensional, but in many settings, the
most relevant information is embedded into only a few relevant dimensions. If
these relevant dimensions were known in advance, the problem would be simple --
but they are not. Moreover, the relevant dimensions may be different for
different actions. Our algorithm learns the relevant dimensions for each
action, and makes decisions based in what it has learned. Formally, we build on
the structure of a contextual multi-armed bandit by adding and exploiting a
relevance relation. We prove a general regret bound for our algorithm whose
time order depends only on the maximum number of relevant dimensions among all
the actions, which in the special case where the relevance relation is
single-valued (a function), reduces to ; in the
absence of a relevance relation, the best known contextual bandit algorithms
achieve regret , where is the full dimension of
the context vector.Comment: to appear in IEEE Journal of Selected Topics in Signal Processing,
201
Learning Aerial Image Segmentation from Online Maps
This study deals with semantic segmentation of high-resolution (aerial)
images where a semantic class label is assigned to each pixel via supervised
classification as a basis for automatic map generation. Recently, deep
convolutional neural networks (CNNs) have shown impressive performance and have
quickly become the de-facto standard for semantic segmentation, with the added
benefit that task-specific feature design is no longer necessary. However, a
major downside of deep learning methods is that they are extremely data-hungry,
thus aggravating the perennial bottleneck of supervised classification, to
obtain enough annotated training data. On the other hand, it has been observed
that they are rather robust against noise in the training labels. This opens up
the intriguing possibility to avoid annotating huge amounts of training data,
and instead train the classifier from existing legacy data or crowd-sourced
maps which can exhibit high levels of noise. The question addressed in this
paper is: can training with large-scale, publicly available labels replace a
substantial part of the manual labeling effort and still achieve sufficient
performance? Such data will inevitably contain a significant portion of errors,
but in return virtually unlimited quantities of it are available in larger
parts of the world. We adapt a state-of-the-art CNN architecture for semantic
segmentation of buildings and roads in aerial images, and compare its
performance when using different training data sets, ranging from manually
labeled, pixel-accurate ground truth of the same city to automatic training
data derived from OpenStreetMap data from distant locations. We report our
results that indicate that satisfying performance can be obtained with
significantly less manual annotation effort, by exploiting noisy large-scale
training data.Comment: Published in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSIN
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