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

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

Egyenlőtlenségek az algebrában és az analízisben = Inequalities in algebra and analysis

Középértékekre vonatkozóan a kétparaméteres Gini- és Stolarsky-féle közepek összehasonlítására találtunk új eredményeket, melyekben az összehasonlítás a paraméterekre vonatkozó egyszerű egyenlőtlenségekkel írható le. Jellemeztük a két és többváltozós integrálközepek szub- és szuperadditivitását. A kétváltozós közepek két nagy osztályában meghatároztuk a homogén közepeket, jellemezve ezáltal a Gini- és Stolarsky-féle közepeket is. Megoldottuk a kétváltozós súlyfüggvénnyel súlyozott kváziaritmetikai és a Cauchy féle közepek egyenlőségproblémáját. Reciprok és szelfinverzív polinomokra több új, az együtthatókban lineáris egyenlőtlenséget találtunk, melyek fennállása biztosítja azt, hogy a polinom összes zérushelye az egységkörvonalon legyen. E feltételekből a zérushelyek elhelyezkedésére és multiplicitására is kapunk információkat. Ezek az eredmények alkalmazhatók az adatátvitelben és véges dimenziós algebrák reprezentációelméletében. A klasszikus Hermite-Hadamard egyenlőtlenséget, és a konvex függvényeket több irányban kiterjesztettük és általánosítottuk. | Mean values. We found new comparison results for the two-parameter Gini and Stolarsky means, where the comparison can be described by simple inequalities in terms of the parameters. Characterized the sub- and superadditive integral means of several variables. Determined the homogeneous means in two large classes of means, by this also a characterization of Gini and Stolarsky means were found. Solved the equality problem of two variable Cauchy and quasiarithmetic means weighted by weight functions. Zeros of reciprocal and self-inversive polynomials. We found a number of new inequalities (linear in the coefficients), which ensure that all zeros of a reciprocal and self-inversive polynomial are on the unit circle. Using these inequalities one can find the location and multiplicities of the zeros. The results are applicable in data transfer and in representation theory of finite dimensional algebras. Classical inequalities. We extended and generalized the Hermite-Hadamard inequality and the concept of convex/concave functions in several directions