16,229 research outputs found
On-line support vector machines for function approximation
This paper describes an on-line method for building epsilon-insensitive support vector machines for regression as described in (Vapnik, 1995). The method is an extension of the method developed by (Cauwenberghs & Poggio, 2000) for building incremental support vector machines for classification. Machines obtained by using this approach are equivalent to the ones obtained by applying exact methods like quadratic programming, but they are obtained more quickly and allow the incremental addition of new points, removal of existing points and update of target values for existing data. This development opens the application of SVM regression to areas such as on-line prediction of temporal series or generalization of value functions in reinforcement learning.Postprint (published version
Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel
Support vector data description (SVDD) is a machine learning technique that
is used for single-class classification and outlier detection. The idea of SVDD
is to find a set of support vectors that defines a boundary around data. When
dealing with online or large data, existing batch SVDD methods have to be rerun
in each iteration. We propose an incremental learning algorithm for SVDD that
uses the Gaussian kernel. This algorithm builds on the observation that all
support vectors on the boundary have the same distance to the center of sphere
in a higher-dimensional feature space as mapped by the Gaussian kernel
function. Each iteration involves only the existing support vectors and the new
data point. Moreover, the algorithm is based solely on matrix manipulations;
the support vectors and their corresponding Lagrange multiplier 's
are automatically selected and determined in each iteration. It can be seen
that the complexity of our algorithm in each iteration is only , where
is the number of support vectors. Experimental results on some real data
sets indicate that FISVDD demonstrates significant gains in efficiency with
almost no loss in either outlier detection accuracy or objective function
value.Comment: 18 pages, 1 table, 4 figure
ODN: Opening the Deep Network for Open-set Action Recognition
In recent years, the performance of action recognition has been significantly
improved with the help of deep neural networks. Most of the existing action
recognition works hold the \textit{closed-set} assumption that all action
categories are known beforehand while deep networks can be well trained for
these categories. However, action recognition in the real world is essentially
an \textit{open-set} problem, namely, it is impossible to know all action
categories beforehand and consequently infeasible to prepare sufficient
training samples for those emerging categories. In this case, applying
closed-set recognition methods will definitely lead to unseen-category errors.
To address this challenge, we propose the Open Deep Network (ODN) for the
open-set action recognition task. Technologically, ODN detects new categories
by applying a multi-class triplet thresholding method, and then dynamically
reconstructs the classification layer and "opens" the deep network by adding
predictors for new categories continually. In order to transfer the learned
knowledge to the new category, two novel methods, Emphasis Initialization and
Allometry Training, are adopted to initialize and incrementally train the new
predictor so that only few samples are needed to fine-tune the model. Extensive
experiments show that ODN can effectively detect and recognize new categories
with little human intervention, thus applicable to the open-set action
recognition tasks in the real world. Moreover, ODN can even achieve comparable
performance to some closed-set methods.Comment: 6 pages, 3 figures, ICME 201
Generalized Kernel-based Visual Tracking
In this work we generalize the plain MS trackers and attempt to overcome
standard mean shift trackers' two limitations.
It is well known that modeling and maintaining a representation of a target
object is an important component of a successful visual tracker.
However, little work has been done on building a robust template model for
kernel-based MS tracking. In contrast to building a template from a single
frame, we train a robust object representation model from a large amount of
data. Tracking is viewed as a binary classification problem, and a
discriminative classification rule is learned to distinguish between the object
and background. We adopt a support vector machine (SVM) for training. The
tracker is then implemented by maximizing the classification score. An
iterative optimization scheme very similar to MS is derived for this purpose.Comment: 12 page
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
This paper contributes improvements on both the effectiveness and efficiency
of Matrix Factorization (MF) methods for implicit feedback. We highlight two
critical issues of existing works. First, due to the large space of unobserved
feedback, most existing works resort to assign a uniform weight to the missing
data to reduce computational complexity. However, such a uniform assumption is
invalid in real-world settings. Second, most methods are also designed in an
offline setting and fail to keep up with the dynamic nature of online data. We
address the above two issues in learning MF models from implicit feedback. We
first propose to weight the missing data based on item popularity, which is
more effective and flexible than the uniform-weight assumption. However, such a
non-uniform weighting poses efficiency challenge in learning the model. To
address this, we specifically design a new learning algorithm based on the
element-wise Alternating Least Squares (eALS) technique, for efficiently
optimizing a MF model with variably-weighted missing data. We exploit this
efficiency to then seamlessly devise an incremental update strategy that
instantly refreshes a MF model given new feedback. Through comprehensive
experiments on two public datasets in both offline and online protocols, we
show that our eALS method consistently outperforms state-of-the-art implicit MF
methods. Our implementation is available at
https://github.com/hexiangnan/sigir16-eals.Comment: 10 pages, 8 figure
- …