2,517 research outputs found
Support vector machine for functional data classification
In many applications, input data are sampled functions taking their values in
infinite dimensional spaces rather than standard vectors. This fact has complex
consequences on data analysis algorithms that motivate modifications of them.
In fact most of the traditional data analysis tools for regression,
classification and clustering have been adapted to functional inputs under the
general name of functional Data Analysis (FDA). In this paper, we investigate
the use of Support Vector Machines (SVMs) for functional data analysis and we
focus on the problem of curves discrimination. SVMs are large margin classifier
tools based on implicit non linear mappings of the considered data into high
dimensional spaces thanks to kernels. We show how to define simple kernels that
take into account the unctional nature of the data and lead to consistent
classification. Experiments conducted on real world data emphasize the benefit
of taking into account some functional aspects of the problems.Comment: 13 page
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
Classification with support hyperplanes
A new classification method is proposed, called Support Hy-perplanes (SHs). To solve the binary classification task, SHs consider theset of all hyperplanes that do not make classification mistakes, referredto as semi-consistent hyperplanes. A test object is classified using thatsemi-consistent hyperplane, which is farthest away from it. In this way, agood balance between goodness-of-fit and model complexity is achieved,where model complexity is proxied by the distance between a test objectand a semi-consistent hyperplane. This idea of complexity resembles theone imputed in the width of the so-called margin between two classes,which arises in the context of Support Vector Machine learning. Classoverlap can be handled via the introduction of kernels and/or slack vari-ables. The performance of SHs against standard classifiers is promisingon several widely-used empirical data sets.Kernel methods;large margin and instance-based classifiers
Inhibition in multiclass classification
The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions,
that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a
classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems.
These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches
Inhibition in multiclass classification
The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions,
that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a
classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems.
These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
Object category localization is a challenging problem in computer vision.
Standard supervised training requires bounding box annotations of object
instances. This time-consuming annotation process is sidestepped in weakly
supervised learning. In this case, the supervised information is restricted to
binary labels that indicate the absence/presence of object instances in the
image, without their locations. We follow a multiple-instance learning approach
that iteratively trains the detector and infers the object locations in the
positive training images. Our main contribution is a multi-fold multiple
instance learning procedure, which prevents training from prematurely locking
onto erroneous object locations. This procedure is particularly important when
using high-dimensional representations, such as Fisher vectors and
convolutional neural network features. We also propose a window refinement
method, which improves the localization accuracy by incorporating an objectness
prior. We present a detailed experimental evaluation using the PASCAL VOC 2007
dataset, which verifies the effectiveness of our approach.Comment: To appear in IEEE Transactions on Pattern Analysis and Machine
Intelligence (TPAMI
A Survey on Graph Kernels
Graph kernels have become an established and widely-used technique for
solving classification tasks on graphs. This survey gives a comprehensive
overview of techniques for kernel-based graph classification developed in the
past 15 years. We describe and categorize graph kernels based on properties
inherent to their design, such as the nature of their extracted graph features,
their method of computation and their applicability to problems in practice. In
an extensive experimental evaluation, we study the classification accuracy of a
large suite of graph kernels on established benchmarks as well as new datasets.
We compare the performance of popular kernels with several baseline methods and
study the effect of applying a Gaussian RBF kernel to the metric induced by a
graph kernel. In doing so, we find that simple baselines become competitive
after this transformation on some datasets. Moreover, we study the extent to
which existing graph kernels agree in their predictions (and prediction errors)
and obtain a data-driven categorization of kernels as result. Finally, based on
our experimental results, we derive a practitioner's guide to kernel-based
graph classification
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