1,200 research outputs found
Multiclass Learning with Simplex Coding
In this paper we discuss a novel framework for multiclass learning, defined
by a suitable coding/decoding strategy, namely the simplex coding, that allows
to generalize to multiple classes a relaxation approach commonly used in binary
classification. In this framework, a relaxation error analysis can be developed
avoiding constraints on the considered hypotheses class. Moreover, we show that
in this setting it is possible to derive the first provably consistent
regularized method with training/tuning complexity which is independent to the
number of classes. Tools from convex analysis are introduced that can be used
beyond the scope of this paper
Totally Corrective Multiclass Boosting with Binary Weak Learners
In this work, we propose a new optimization framework for multiclass boosting
learning. In the literature, AdaBoost.MO and AdaBoost.ECC are the two
successful multiclass boosting algorithms, which can use binary weak learners.
We explicitly derive these two algorithms' Lagrange dual problems based on
their regularized loss functions. We show that the Lagrange dual formulations
enable us to design totally-corrective multiclass algorithms by using the
primal-dual optimization technique. Experiments on benchmark data sets suggest
that our multiclass boosting can achieve a comparable generalization capability
with state-of-the-art, but the convergence speed is much faster than stage-wise
gradient descent boosting. In other words, the new totally corrective
algorithms can maximize the margin more aggressively.Comment: 11 page
RandomBoost: Simplified Multi-class Boosting through Randomization
We propose a novel boosting approach to multi-class classification problems,
in which multiple classes are distinguished by a set of random projection
matrices in essence. The approach uses random projections to alleviate the
proliferation of binary classifiers typically required to perform multi-class
classification. The result is a multi-class classifier with a single
vector-valued parameter, irrespective of the number of classes involved. Two
variants of this approach are proposed. The first method randomly projects the
original data into new spaces, while the second method randomly projects the
outputs of learned weak classifiers. These methods are not only conceptually
simple but also effective and easy to implement. A series of experiments on
synthetic, machine learning and visual recognition data sets demonstrate that
our proposed methods compare favorably to existing multi-class boosting
algorithms in terms of both the convergence rate and classification accuracy.Comment: 15 page
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