Sharing features in multi-class boosting via group sparsity

We present a novel formulation of fully corrective boosting for multi-class classification problems with the awareness of sharing features. Our multi-class boosting is solved in a single optimization problem. In order to share features across different classes, we introduce the mixed-norm regularization, which promotes group sparsity, into boosting. We then derive the Lagrange dual problems which enable us to design fully corrective multi-class algorithms using the primal-dual optimization technique. We show that sharing features across classes can improve classification performance and efficiency. We empirically show that in many cases, the proposed multi-class boosting generalizes better than a range of competing multi-class boosting algorithms due to the capability of feature sharing. Experimental results on machine learning data, visual scene and object recognition demonstrate the efficiency and effectiveness of proposed algorithms and validate our theoretical findings.Sakrapee Paisitkriangkrai, Chunhua Shen and Anton van den Henge

Semantic enrichment of knowledge sources supported by domain ontologies

This thesis introduces a novel conceptual framework to support the creation of knowledge representations based on enriched Semantic Vectors, using the classical vector space model approach extended with ontological support. One of the primary research challenges addressed here relates to the process of formalization and representation of document contents, where most existing approaches are limited and only take into account the explicit, word-based information in the document. This research explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships (semantic associations) modelled by domain ontologies with the addition of information presented in documents. The relevant achievements pursued by this thesis are the following: (i) conceptualization of a model that enables the semantic enrichment of knowledge sources supported by domain experts; (ii) development of a method for extending the traditional vector space, using domain ontologies; (iii) development of a method to support ontology learning, based on the discovery of new ontological relations expressed in non-structured information sources; (iv) development of a process to evaluate the semantic enrichment; (v) implementation of a proof-of-concept, named SENSE (Semantic Enrichment kNowledge SourcEs), which enables to validate the ideas established under the scope of this thesis; (vi) publication of several scientific articles and the support to 4 master dissertations carried out by the department of Electrical and Computer Engineering from FCT/UNL. It is worth mentioning that the work developed under the semantic referential covered by this thesis has reused relevant achievements within the scope of research European projects, in order to address approaches which are considered scientifically sound and coherent and avoid “reinventing the wheel”.European research projects - CoSpaces (IST-5-034245), CRESCENDO (FP7-234344) and MobiS (FP7-318452

MevaL: A Visual Machine Learning Model Evaluation Tool for Financial Crime Detection

Data Science and Machine Learning are two valuable allies to fight financial crime,the domain where Feedzai seeks to leverage its value proposition in support of its mission:to make banking and commerce safe. Data is at the core of both fields and this domain, sostructuring instances for visual consumption provides an effective way of understandingthe data and communicating insights.The development of a solution for each project and use case requires a careful andeffective Machine Learning Model Evaluation stage, as it is the major source of feedbackbefore deployment. The tooling for this stage available at Feedzai can be improved,accelerated, visually supported, and diversified to enable data scientists to boost theirdaily work and the quality of the models.In this work, I propose to collect and compile internal and external input, in terms ofworkflow and Model Evaluation, in a proposal hierarchically segmented by well-definedobjectives and tasks, to instantiate the proposal in a Python package, and to iteratively val-idate the package with Feedzai’s data scientists. Therefore, the first contribution is MevaL,a Python package for Model Evaluation with visual support, integrated into Feedzai’s DataScience environment by design. In fact, MevaL is already being leveraged as a visualization package on two internal reporting projects that are serving some of Feedzai’s majorclients.In addition to MevaL, the second contribution of this work is the Model EvaluationTopology developed to ensure clear communication and design of features.A Ciência de Dados e a Aprendizagem Automática [277] são duas valiosas aliadas no combate à criminalidade económico-financeira, o domínio em que a Feedzai procura potenciar a sua proposta de valor em prol da sua missão: tornar o sistema bancário e o comércio seguros. Além disso, os dados estão no centro das duas áreas e deste domínio.Assim, a estruturação visual dos mesmos fornece uma maneira eficaz de os entender e transmitir informação.O desenvolvimento de uma solução para cada projeto e caso de uso requer um estágiocuidadoso e eficaz de Avaliação de Modelos de Aprendizagem Automática, pois esteestágio coincide com a principal fonte de retorno (feedback) antes da implementaçãoda solução. As ferramentas de Avaliação de Modelos disponíveis na Feedzai podem seraprimoradas, aceleradas, suportadas visualmente e diversificadas para permitir que oscientistas de dados impulsionem o seu trabalho diário e a qualidade destes modelos.Neste trabalho, proponho a recolha e compilação de informação interna e externa, em termos de fluxo de trabalho e Avaliação de Modelos, numa proposta hierarquicamente segmentada por objetivos e tarefas bem definidas, a instanciação desta proposta num pacote Python e a validação iterativa deste pacote em colaboração com os cientistas de dados da Feedzai. Posto isto, a primeira contribuição deste trabalho é o MevaL, um pacote Python para Avaliação de Modelos com suporte visual, integrado no ambiente de Ciência de Dados da Feedzai. Na verdade, o MevaL já está a ser utilizado como um pacote de visualização em dois projetos internos de preparação de relatórios automáticos para alguns dos principais clientes da Feedzai.Além do MevaL, a segunda contribuição deste trabalho é a Topologia de Avaliação de Modelos desenvolvida para garantir uma comunicação clara e o design enquadrado das diferentes funcionalidades

THE ROLE OF TEXTURE IN INDOOR SCENE RECOGNITION

Ph.DDOCTOR OF PHILOSOPH