2 research outputs found
Deep Self-Taught Learning for Handwritten Character Recognition
Recent theoretical and empirical work in statistical machine learning has
demonstrated the importance of learning algorithms for deep architectures,
i.e., function classes obtained by composing multiple non-linear
transformations. Self-taught learning (exploiting unlabeled examples or
examples from other distributions) has already been applied to deep learners,
but mostly to show the advantage of unlabeled examples. Here we explore the
advantage brought by {\em out-of-distribution examples}. For this purpose we
developed a powerful generator of stochastic variations and noise processes for
character images, including not only affine transformations but also slant,
local elastic deformations, changes in thickness, background images, grey level
changes, contrast, occlusion, and various types of noise. The
out-of-distribution examples are obtained from these highly distorted images or
by including examples of object classes different from those in the target test
set. We show that {\em deep learners benefit more from out-of-distribution
examples than a corresponding shallow learner}, at least in the area of
handwritten character recognition. In fact, we show that they beat previously
published results and reach human-level performance on both handwritten digit
classification and 62-class handwritten character recognition
Prédiction de l'attrition en date de renouvellement en assurance automobile avec processus gaussiens
Le domaine de l’assurance automobile fonctionne par cycles présentant des phases
de profitabilité et d’autres de non-profitabilité. Dans les phases de non-profitabilité, les
compagnies d’assurance ont généralement le réflexe d’augmenter le coût des primes
afin de tenter de réduire les pertes. Par contre, de très grandes augmentations peuvent
avoir pour effet de massivement faire fuir la clientèle vers les compétiteurs. Un trop
haut taux d’attrition pourrait avoir un effet négatif sur la profitabilité à long terme de la
compagnie. Une bonne gestion des augmentations de taux se révèle donc primordiale
pour une compagnie d’assurance.
Ce mémoire a pour but de construire un outil de simulation de l’allure du porte-
feuille d’assurance dĂ©tenu par un assureur en fonction du changement de taux proposĂ© Ă
chacun des assurés. Une procédure utilisant des régressions à l’aide de processus gaus-
siens univariés est développée. Cette procédure offre une performance supérieure à la
régression logistique, le modèle généralement utilisé pour effectuer ce genre de tâche.The field of auto insurance is working by cycles with phases of profitability and other
of non-profitability. In the phases of non-profitability, insurance companies generally
have the reflex to increase the cost of premiums in an attempt to reduce losses. For cons,
very large increases may have the effect of massive attrition of the customers. A too
high attrition rate could have a negative effect on long-term profitability of the company.
Proper management of rate increases thus appears crucial to an insurance company.
This thesis aims to build a simulation tool to predict the content of the insurance
portfolio held by an insurer based on the rate change proposed to each insured. A proce-
dure using univariate Gaussian Processes regression is developed. This procedure offers
a superior performance than the logistic regression model typically used to perform such
tasks