Modeling brand choice using boosted and stacked neural networks

The brand choice problem in marketing has recently been addressed with methods from computational intelligence such as neural networks. Another class of methods from computational intelligence, the so-called ensemble methods such as boosting and stacking have never been applied to the brand choice problem, as far as we know. Ensemble methods generate a number of models for the same problem using any base method and combine the outcomes of these different models. It is well known that in many cases the predictive performance of ensemble methods significantly exceeds the predictive performance of the their base methods. In this report we use boosting and stacking of neural networks and apply this to a scanner dataset that is a benchmark dataset in the marketing literature. Using these methods, we find a significant improvement in predictive performance on this dataset.

Hoogbegaafde jongeren en zingeving. Een onderzoek naar de rol van zingeving wanneer hoogbegaafde jongeren uitvallen van school en zich ontwikkelen binnen het Centrum voor Creatief Leren

Dit onderzoeksverslag bestaat uit een toepassing van een conceptuele doordenking van zingeving in een praktijk van een specifieke doelgroep, hoogbegaafde drop-outs, opgevangen door het Centrum voor Creatief Leren (CCL) te Sterksel. De hoofdvraag was Welke aspecten van zingeving (volgens Alma & Smaling, 2010) spelen een rol in het ontwikkelingsproces dat hoogbegaafde drop-outs doorlopen binnen het Centrum voor Creatief Leren en komt dit overeen met de behoeften van de jongeren ten aanzien van zingeving? Bij het onderzoek is gebruik gemaakt van triangulatie, waarbij vier methoden van onderzoek werden uitgevoerd. Het eerste deel bestond uit participerende observatie binnen het CCL. Het tweede deel besloeg een dossieronderzoek, waarbij elf dossiers zijn onderzocht en geanalyseerd op zingevingsaspecten. Het derde en vierde deel bestond uit het interviewen van twee verschillende doelgroepen: de medewerkers van het CCL en de leerlingen binnen het CCL. De leerlingen zijn door middel van een focusgroep (groepsinterview) onderzocht. Het onderzoeksinstrument bestond uit de theorie over zingeving van Alma en Smaling (2010), gekenmerkt door negen zingevingsaspecten; doelgerichtheid, samenhang, waardevolheid, verbondenheid, transcendentie, competentie, erkenning, motiverende werking en welbevinden. Uit de resultaten kwam dat transcendentie (aan de hand van een levensbeschouwelijk kader) geen primaire levensbehoefte is voor deze doelgroep. Voor hen spelen allereerst erkenning, eigenwaarde en verbondenheid een belangrijke rol in het vormgeven en vinden van hun eigen zingeving

Modeling brand choice using boosted and stacked neural networks

The brand choice problem in marketing has recently been addressed with methods from computational intelligence such as neural networks. Another class of methods from computational intelligence, the so-called ensemble methods such as boosting and stacking have never been applied to the brand choice problem, as far as we know. Ensemble methods generate a number of models for the same problem using any base method and combine the outcomes of these different models. It is well known that in many cases the predictive performance of ensemble methods significantly exceeds the predictive performance of the their base methods. In this report we use boosting and stacking of neural networks and apply this to a scanner dataset that is a benchmark dataset in the marketing literature. Using these methods, we find a significant improvement in predictive performance on this dataset

HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images

We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks. Concentricpatches at multiple resolutions with different fields of view are used to feed different branches of HookNet, and intermediate representations are combined via a hooking mechanism. We describe a framework to design and train HookNet for achieving high-resolution semantic segmentation and introduce constraints to guarantee pixel-wise alignment in feature maps during hooking. We show the advantages of using HookNet in two histopathology image segmentation tasks where tissue type prediction accuracy strongly depends on contextual information, namely (1) multi-class tissue segmentation in breast cancer and, (2) segmentation of tertiary lymphoid structures and germinal centers in lung cancer. Weshow the superiority of HookNet when compared with single-resolution U-Net models working at different resolutions as well as with a recently published multi-resolution model for histopathology image segmentatio

DIAGNijmegen/pathology-hooknet-tls: v0.0.1

No description provided

Compensating reading and spelling abilities in children with dyslexia

Learning how to read and spell requires a closely knit knowledge network with an understanding of meaning (semantics), sound (phonology) and letters (orthography). The more intricate this network, the easier it is to learn how to read and spell. Children with dyslexia often struggle to develop the phonological area of this network. The resulting phonological deficits can lead to reading and spelling problems. In his dissertation, Robin van Rijthoven explored whether children could use their strengths in the other two areas to limit the damage in this area. As it happens, they can. Van Rijthoven found that children with a strong semantic network were better readers and spellers and more capable of improving their spelling skills during treatment than children with a weaker network. He also found that children with dyslexia, regardless of their phonological deficits, benefited from an integrated approach to reading and spelling with a relatively strong focus on spelling. Stimulating the orthographic network also appeared to have a compensatory effect

Cell Micro-Rheology Under Hypergravity Conditions

Biomedical Engineerin