58,032 research outputs found
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks
How can we reuse existing knowledge, in the form of available datasets, when
solving a new and apparently unrelated target task from a set of unlabeled
data? In this work we make a first contribution to answer this question in the
context of image classification. We frame this quest as an active learning
problem and use zero-shot classifiers to guide the learning process by linking
the new task to the existing classifiers. By revisiting the dual formulation of
adaptive SVM, we reveal two basic conditions to choose greedily only the most
relevant samples to be annotated. On this basis we propose an effective active
learning algorithm which learns the best possible target classification model
with minimum human labeling effort. Extensive experiments on two challenging
datasets show the value of our approach compared to the state-of-the-art active
learning methodologies, as well as its potential to reuse past datasets with
minimal effort for future tasks
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
Detection of Dispersed Radio Pulses: A machine learning approach to candidate identification and classification
Searching for extraterrestrial, transient signals in astronomical data sets
is an active area of current research. However, machine learning techniques are
lacking in the literature concerning single-pulse detection. This paper
presents a new, two-stage approach for identifying and classifying dispersed
pulse groups (DPGs) in single-pulse search output. The first stage identified
DPGs and extracted features to characterize them using a new peak
identification algorithm which tracks sloping tendencies around local maxima in
plots of signal-to-noise ratio vs. dispersion measure. The second stage used
supervised machine learning to classify DPGs. We created four benchmark data
sets: one unbalanced and three balanced versions using three different
imbalance treatments.We empirically evaluated 48 classifiers by training and
testing binary and multiclass versions of six machine learning algorithms on
each of the four benchmark versions. While each classifier had advantages and
disadvantages, all classifiers with imbalance treatments had higher recall
values than those with unbalanced data, regardless of the machine learning
algorithm used. Based on the benchmarking results, we selected a subset of
classifiers to classify the full, unlabelled data set of over 1.5 million DPGs
identified in 42,405 observations made by the Green Bank Telescope. Overall,
the classifiers using a multiclass ensemble tree learner in combination with
two oversampling imbalance treatments were the most efficient; they identified
additional known pulsars not in the benchmark data set and provided six
potential discoveries, with significantly less false positives than the other
classifiers.Comment: 13 pages, accepted for publication in MNRAS, ref. MN-15-1713-MJ.R
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