480 research outputs found
Training Support Vector Machines Using Frank-Wolfe Optimization Methods
Training a Support Vector Machine (SVM) requires the solution of a quadratic
programming problem (QP) whose computational complexity becomes prohibitively
expensive for large scale datasets. Traditional optimization methods cannot be
directly applied in these cases, mainly due to memory restrictions.
By adopting a slightly different objective function and under mild conditions
on the kernel used within the model, efficient algorithms to train SVMs have
been devised under the name of Core Vector Machines (CVMs). This framework
exploits the equivalence of the resulting learning problem with the task of
building a Minimal Enclosing Ball (MEB) problem in a feature space, where data
is implicitly embedded by a kernel function.
In this paper, we improve on the CVM approach by proposing two novel methods
to build SVMs based on the Frank-Wolfe algorithm, recently revisited as a fast
method to approximate the solution of a MEB problem. In contrast to CVMs, our
algorithms do not require to compute the solutions of a sequence of
increasingly complex QPs and are defined by using only analytic optimization
steps. Experiments on a large collection of datasets show that our methods
scale better than CVMs in most cases, sometimes at the price of a slightly
lower accuracy. As CVMs, the proposed methods can be easily extended to machine
learning problems other than binary classification. However, effective
classifiers are also obtained using kernels which do not satisfy the condition
required by CVMs and can thus be used for a wider set of problems
Exploring the boundaries of vinylogous Mukaiyama aldol processes: stereoselective access to polyunsaturated homoallylic alcohols
Catalytic enantioselective vinylogous aldol reactions using extended enolates are of prominent value in synthetic organic chemistry. Here, we report our advances in the
development of enantioselective bis-vinylogous and hyper-vinylogous Mukaiyama aldol reactions between a series of polyenylsilyloxy furans or polyenylsilyoxy indoles and
aromatic aldehydes, realized by use of the enabling catalyst combination of silicon tetrachloride and Denmark’s chiral bis-phosphoramide base (R,R)-I. Several crucial
issues such as the remote site-, enantio- and geometrical selectivity of the reaction will be highlighted, ultimately focusing on one main question: how far can we push the
limits of the vinylogous reactivity transmittal?</i
Personalized Web Search via Query Expansion based on User’s Local Hierarchically-Organized Files
Users of Web search engines generally express information needs with short and ambiguous queries, leading to irrelevant results. Personalized search methods improve users’ experience by automatically reformulating queries before sending them to the search engine or rearranging received results, according to their specific interests. A user profile is often built from previous queries, clicked results or in general from the user’s browsing history; different topics must be distinguished in order to obtain an accurate profile. It is quite common that a set of user files, locally stored in sub-directory, are organized by the user into a coherent taxonomy corresponding to own topics of interest, but only a few methods leverage on this potentially useful source of knowledge. We propose a novel method where a user profile is built from those files, specifically considering their consistent arrangement in directories. A bag of keywords is extracted for each directory from text documents with in it. We can infer the topic of each query and expand it by adding the corresponding keywords, in order to obtain a more targeted formulation. Experiments are carried out using benchmark data through a repeatable systematic process, in order to evaluate objectively how much our method can improve relevance of query results when applied upon a third-party search engin
A Novel Frank-Wolfe Algorithm. Analysis and Applications to Large-Scale SVM Training
Recently, there has been a renewed interest in the machine learning community
for variants of a sparse greedy approximation procedure for concave
optimization known as {the Frank-Wolfe (FW) method}. In particular, this
procedure has been successfully applied to train large-scale instances of
non-linear Support Vector Machines (SVMs). Specializing FW to SVM training has
allowed to obtain efficient algorithms but also important theoretical results,
including convergence analysis of training algorithms and new characterizations
of model sparsity.
In this paper, we present and analyze a novel variant of the FW method based
on a new way to perform away steps, a classic strategy used to accelerate the
convergence of the basic FW procedure. Our formulation and analysis is focused
on a general concave maximization problem on the simplex. However, the
specialization of our algorithm to quadratic forms is strongly related to some
classic methods in computational geometry, namely the Gilbert and MDM
algorithms.
On the theoretical side, we demonstrate that the method matches the
guarantees in terms of convergence rate and number of iterations obtained by
using classic away steps. In particular, the method enjoys a linear rate of
convergence, a result that has been recently proved for MDM on quadratic forms.
On the practical side, we provide experiments on several classification
datasets, and evaluate the results using statistical tests. Experiments show
that our method is faster than the FW method with classic away steps, and works
well even in the cases in which classic away steps slow down the algorithm.
Furthermore, these improvements are obtained without sacrificing the predictive
accuracy of the obtained SVM model.Comment: REVISED VERSION (October 2013) -- Title and abstract have been
revised. Section 5 was added. Some proofs have been summarized (full-length
proofs available in the previous version
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