Peter van der Putten

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There is a range of techniques available to reverse engineer software designs from source code. However, these approaches generate highly detailed representations. The condensing of reverse engineered representations into more high-level design information would enhance the understandability of reverse engineered diagrams. This paper describes an automated(More)
Artificial Immune Systems are a new class of algorithms inspired by how the immune system recognizes, attacks and remembers intruders. This is a fascinating idea, but to be accepted for mainstream data mining applications, extensive benchmarking is needed to demonstrate the reliability and accuracy of these algorithms. In our research we focus on the AIRS(More)
In this paper the first known case is presented in which an antepartum diagnosis of placenta membranacea was made by ultrasound. A multiparous woman is presented with intermittent painless vaginal bleeding in the second trimester of pregnancy. Ultrasonic examination at 20 weeks' gestation revealed a gestational sac almost completely covered with placental(More)
In direct marketing large amounts of customer data are collected that might have some complex, non linear relation to customer behavior. Data mining techniques can ooer insight in these relations. In this paper we give a basic introduction in the application of data mining to direct marketing. Best practices for data selection, algorithm selection and(More)
This paper introduces a real time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification consists of a two stage procedure. First, small image fragments called(More)
Maximum quantum yield for leaf CO2 assimilation under limiting light conditions (Φ CO2LL) is commonly estimated as the slope of the linear regression of net photosynthetic rate against absorbed irradiance over a range of low-irradiance conditions. Methodological errors associated with this estimation have often been attributed either to light absorptance by(More)
The CoIL Challenge 2000 data mining competition attracted a wide variety of solutions, both in terms of approaches and performance. The goal of the competition was to predict who would be interested in buying a specific insurance product and to explain why people would buy. Unlike in most other competitions, the majority of participants provided a report(More)
In this thesis we study the effect of target set size on transfer learning in deep learning convolutional neural networks. This is an important problem as labelling is a costly task, or for new or specific classes the number of labelled instances available may simply be too small. We first discuss feedforward neural networks and convolutional networks to(More)
Customer churn, i.e., losing a customer to the competition, is a major problem in mobile telecommunications. This paper investigates the added value of combining regular tabular data mining with social network mining, leveraging the graph formed by communications between customers. We extend classical tabular churn datasets with predictors derived from(More)
Prepaid customers in mobile telecommunications are not bound by a contract and can therefore change operators (‘churn’) at their convenience and without notification. This makes the task of predicting prepaid churn both challenging and financially rewarding. This paper presents an explorative, real world study of prepaid churn modeling by varying the(More)