51 research outputs found
Machine learning methods for multimedia information retrieval
In this thesis we examined several multimodal feature extraction and learning
methods for retrieval and classification purposes. We reread briefly some
theoretical results of learning in Section 2 and reviewed several generative
and discriminative models in Section 3 while we described the similarity kernel
in Section 4. We examined different aspects of the multimodal image retrieval
and classification in Section 5 and suggested methods for identifying quality
assessments of Web documents in Section 6. In our last problem we proposed
similarity kernel for time-series based classification. The experiments were
carried over publicly available datasets and source codes for the most
essential parts are either open source or released. Since the used similarity
graphs (Section 4.2) are greatly constrained for computational purposes, we
would like to continue work with more complex, evolving and capable graphs and
apply for different problems such as capturing the rapid change in the
distribution (e.g. session based recommendation) or complex graphs of the
literature work. The similarity kernel with the proper metrics reaches and in
many cases improves over the state-of-the-art. Hence we may conclude generative
models based on instance similarities with multiple modes is a generally
applicable model for classification and regression tasks ranging over various
domains, including but not limited to the ones presented in this thesis. More
generally, the Fisher kernel is not only unique in many ways but one of the
most powerful kernel functions. Therefore we may exploit the Fisher kernel in
the future over widely used generative models, such as Boltzmann Machines
[Hinton et al., 1984], a particular subset, the Restricted Boltzmann Machines
and Deep Belief Networks [Hinton et al., 2006]), Latent Dirichlet Allocation
[Blei et al., 2003] or Hidden Markov Models [Baum and Petrie, 1966] to name a
few.Comment: doctoral thesis, 201
PAC bounds of continuous Linear Parameter-Varying systems related to neural ODEs
We consider the problem of learning Neural Ordinary Differential Equations
(neural ODEs) within the context of Linear Parameter-Varying (LPV) systems in
continuous-time. LPV systems contain bilinear systems which are known to be
universal approximators for non-linear systems. Moreover, a large class of
neural ODEs can be embedded into LPV systems. As our main contribution we
provide Probably Approximately Correct (PAC) bounds under stability for LPV
systems related to neural ODEs. The resulting bounds have the advantage that
they do not depend on the integration interval.Comment: 12 page
Content-based trust and bias classification via biclustering
In this paper we improve trust, bias and factuality classification over Web data on the domain level. Unlike the majority of literature in this area that aims at extracting opinion and handling short text on the micro level, we aim to aid a researcher or an archivist in obtaining a large collection that, on the high level, originates from unbiased and trustworthy sources. Our method generates features as Jensen-Shannon distances from centers in a host-term biclustering. On top of the distance features, we apply kernel methods and also combine with baseline text classifiers. We test our method on the ECML/PKDD Discovery Challenge data set DC2010. Our method improves over the best achieved text classification NDCG results by over 3--10% for neutrality, bias and trustworthiness. The fact that the ECML/PKDD Discovery Challenge 2010 participants reached an AUC only slightly above 0.5 indicates the hardness of the task
Machine learning based session drop prediction in LTE networks and its SON aspects
Abnormal bearer session release (i.e. bearer session drop) in cellular telecommunication networks may seriously impact the quality of experience of mobile users. The latest mobile technologies enable high granularity real-time reporting of all conditions of individual sessions, which gives rise to use data analytics methods to process and monetize this data for network optimization. One such example for analytics is Machine Learning (ML) to predict session drops well before the end of session. In this paper a novel ML method is presented that is able to predict session drops with higher accuracy than using traditional models. The method is applied and tested on live LTE data offline. The high accuracy predictor can be part of a SON function in order to eliminate the session drops or mitigate their effects. © 2015 IEEE
Gradient representations in ReLU networks as similarity functions
Feed-forward networks can be interpreted as mappings with linear decision
surfaces at the level of the last layer. We investigate how the tangent space
of the network can be exploited to refine the decision in case of ReLU
(Rectified Linear Unit) activations. We show that a simple Riemannian metric
parametrized on the parameters of the network forms a similarity function at
least as good as the original network and we suggest a sparse metric to
increase the similarity gap.Comment: Accepted at 29th ESANN 2021, 6-8 October 2021, Belgium, 7 pages, 1
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