1,026,343 research outputs found
Do not forget: Full memory in memory-based learning of word pronunciation
Memory-based learning, keeping full memory of learning material, appears a
viable approach to learning NLP tasks, and is often superior in generalisation
accuracy to eager learning approaches that abstract from learning material.
Here we investigate three partial memory-based learning approaches which remove
from memory specific task instance types estimated to be exceptional. The three
approaches each implement one heuristic function for estimating exceptionality
of instance types: (i) typicality, (ii) class prediction strength, and (iii)
friendly-neighbourhood size. Experiments are performed with the memory-based
learning algorithm IB1-IG trained on English word pronunciation. We find that
removing instance types with low prediction strength (ii) is the only tested
method which does not seriously harm generalisation accuracy. We conclude that
keeping full memory of types rather than tokens, and excluding minority
ambiguities appear to be the only performance-preserving optimisations of
memory-based learning.Comment: uses conll98, epsf, and ipamacs (WSU IPA
A Convex Relaxation for Weakly Supervised Classifiers
This paper introduces a general multi-class approach to weakly supervised
classification. Inferring the labels and learning the parameters of the model
is usually done jointly through a block-coordinate descent algorithm such as
expectation-maximization (EM), which may lead to local minima. To avoid this
problem, we propose a cost function based on a convex relaxation of the
soft-max loss. We then propose an algorithm specifically designed to
efficiently solve the corresponding semidefinite program (SDP). Empirically,
our method compares favorably to standard ones on different datasets for
multiple instance learning and semi-supervised learning as well as on
clustering tasks.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
Learning and Interpreting Multi-Multi-Instance Learning Networks
We introduce an extension of the multi-instance learning problem where
examples are organized as nested bags of instances (e.g., a document could be
represented as a bag of sentences, which in turn are bags of words). This
framework can be useful in various scenarios, such as text and image
classification, but also supervised learning over graphs. As a further
advantage, multi-multi instance learning enables a particular way of
interpreting predictions and the decision function. Our approach is based on a
special neural network layer, called bag-layer, whose units aggregate bags of
inputs of arbitrary size. We prove theoretically that the associated class of
functions contains all Boolean functions over sets of sets of instances and we
provide empirical evidence that functions of this kind can be actually learned
on semi-synthetic datasets. We finally present experiments on text
classification, on citation graphs, and social graph data, which show that our
model obtains competitive results with respect to accuracy when compared to
other approaches such as convolutional networks on graphs, while at the same
time it supports a general approach to interpret the learnt model, as well as
explain individual predictions.Comment: JML
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