71,306 research outputs found
On the Bayes-optimality of F-measure maximizers
The F-measure, which has originally been introduced in information retrieval,
is nowadays routinely used as a performance metric for problems such as binary
classification, multi-label classification, and structured output prediction.
Optimizing this measure is a statistically and computationally challenging
problem, since no closed-form solution exists. Adopting a decision-theoretic
perspective, this article provides a formal and experimental analysis of
different approaches for maximizing the F-measure. We start with a Bayes-risk
analysis of related loss functions, such as Hamming loss and subset zero-one
loss, showing that optimizing such losses as a surrogate of the F-measure leads
to a high worst-case regret. Subsequently, we perform a similar type of
analysis for F-measure maximizing algorithms, showing that such algorithms are
approximate, while relying on additional assumptions regarding the statistical
distribution of the binary response variables. Furthermore, we present a new
algorithm which is not only computationally efficient but also Bayes-optimal,
regardless of the underlying distribution. To this end, the algorithm requires
only a quadratic (with respect to the number of binary responses) number of
parameters of the joint distribution. We illustrate the practical performance
of all analyzed methods by means of experiments with multi-label classification
problems
Conditional Restricted Boltzmann Machines for Structured Output Prediction
Conditional Restricted Boltzmann Machines (CRBMs) are rich probabilistic
models that have recently been applied to a wide range of problems, including
collaborative filtering, classification, and modeling motion capture data.
While much progress has been made in training non-conditional RBMs, these
algorithms are not applicable to conditional models and there has been almost
no work on training and generating predictions from conditional RBMs for
structured output problems. We first argue that standard Contrastive
Divergence-based learning may not be suitable for training CRBMs. We then
identify two distinct types of structured output prediction problems and
propose an improved learning algorithm for each. The first problem type is one
where the output space has arbitrary structure but the set of likely output
configurations is relatively small, such as in multi-label classification. The
second problem is one where the output space is arbitrarily structured but
where the output space variability is much greater, such as in image denoising
or pixel labeling. We show that the new learning algorithms can work much
better than Contrastive Divergence on both types of problems
Learning Structured Inference Neural Networks with Label Relations
Images of scenes have various objects as well as abundant attributes, and
diverse levels of visual categorization are possible. A natural image could be
assigned with fine-grained labels that describe major components,
coarse-grained labels that depict high level abstraction or a set of labels
that reveal attributes. Such categorization at different concept layers can be
modeled with label graphs encoding label information. In this paper, we exploit
this rich information with a state-of-art deep learning framework, and propose
a generic structured model that leverages diverse label relations to improve
image classification performance. Our approach employs a novel stacked label
prediction neural network, capturing both inter-level and intra-level label
semantics. We evaluate our method on benchmark image datasets, and empirical
results illustrate the efficacy of our model.Comment: Conference on Computer Vision and Pattern Recognition(CVPR) 201
The Lov\'asz Hinge: A Novel Convex Surrogate for Submodular Losses
Learning with non-modular losses is an important problem when sets of
predictions are made simultaneously. The main tools for constructing convex
surrogate loss functions for set prediction are margin rescaling and slack
rescaling. In this work, we show that these strategies lead to tight convex
surrogates iff the underlying loss function is increasing in the number of
incorrect predictions. However, gradient or cutting-plane computation for these
functions is NP-hard for non-supermodular loss functions. We propose instead a
novel surrogate loss function for submodular losses, the Lov\'asz hinge, which
leads to O(p log p) complexity with O(p) oracle accesses to the loss function
to compute a gradient or cutting-plane. We prove that the Lov\'asz hinge is
convex and yields an extension. As a result, we have developed the first
tractable convex surrogates in the literature for submodular losses. We
demonstrate the utility of this novel convex surrogate through several set
prediction tasks, including on the PASCAL VOC and Microsoft COCO datasets
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