55 research outputs found
A finder and representation system for knowledge carriers based on granular computing
In one of his publications Aristotle states ”All human beings by their nature desire to know” [Kraut 1991]. This desire is initiated the day we are born and accompanies us for the rest of our life. While at a young age our parents serve as one of the principle sources for knowledge, this changes over the course of time. Technological advances and particularly the introduction of the Internet, have given us new possibilities to share and access knowledge from almost anywhere at any given time. Being able to access and share large collections of written down knowledge is only one part of the equation. Just as important is the internalization of it, which in many cases can prove to be difficult to accomplish. Hence, being able to request assistance from someone who holds the necessary knowledge is of great importance, as it can positively stimulate the internalization procedure. However, digitalization does not only provide a larger pool of knowledge sources to choose from but also more people that can be potentially activated, in a bid to receive personalized assistance with a given problem statement or question. While this is beneficial, it imposes the issue that it is hard to keep track of who knows what. For this task so-called Expert Finder Systems have been introduced, which are designed to identify and suggest the most suited candidates to provide assistance. Throughout this Ph.D. thesis a novel type of Expert Finder System will be introduced that is capable of capturing the knowledge users within a community hold, from explicit and implicit data sources. This is accomplished with the use of granular computing, natural language processing and a set of metrics that have been introduced to measure and compare the suitability of candidates. Furthermore, are the knowledge requirements of a problem statement or question being assessed, in order to ensure that only the most suited candidates are being recommended to provide assistance
Integrating domain knowledge: using hierarchies to improve deep classifiers
One of the most prominent problems in machine learning in the age of deep
learning is the availability of sufficiently large annotated datasets. While
for standard problem domains (ImageNet classification), appropriate datasets
exist, for specific domains, \eg classification of animal species, a long-tail
distribution means that some classes are observed and annotated insufficiently.
Challenges like iNaturalist show that there is a strong interest in species
recognition. Acquiring additional labels can be prohibitively expensive. First,
since domain experts need to be involved, and second, because acquisition of
new data might be costly. Although there exist methods for data augmentation,
which not always lead to better performance of the classifier, there is more
additional information available that is to the best of our knowledge not
exploited accordingly.
In this paper, we propose to make use of existing class hierarchies like
WordNet to integrate additional domain knowledge into classification. We encode
the properties of such a class hierarchy into a probabilistic model. From
there, we derive a special label encoding together with a corresponding loss
function. Using a convolutional neural network, on the ImageNet and NABirds
datasets our method offers a relative improvement of 10.4% and 9.6% in accuracy
over the baseline respectively. After less than a third of training time, it is
already able to match the baseline's fine-grained recognition performance. Both
results show that our suggested method is efficient and effective.Comment: 9 pages, 7 figure
Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding
Classifying single image patches is important in many different applications,
such as road detection or scene understanding. In this paper, we present
convolutional patch networks, which are convolutional networks learned to
distinguish different image patches and which can be used for pixel-wise
labeling. We also show how to incorporate spatial information of the patch as
an input to the network, which allows for learning spatial priors for certain
categories jointly with an appearance model. In particular, we focus on road
detection and urban scene understanding, two application areas where we are
able to achieve state-of-the-art results on the KITTI as well as on the
LabelMeFacade dataset.
Furthermore, our paper offers a guideline for people working in the area and
desperately wandering through all the painstaking details that render training
CNs on image patches extremely difficult.Comment: VISAPP 2015 pape
From Default Probabilities To Credit Spreads: Credit Risk Models Do Explain Market Prices
Credit risk models like Moody’s KMV are now well established in the market and give bond managers reliable estimates of default probabilities for individual firms. Until now it has been hard to relate those probabilities to the actual credit spreads observed on the market for corporate bonds. Inspired by the existence of scaling laws in financial markets by Dacorogna et al. (2001) and Di Matteo et al. (2005) deviating from the Gaussian behavior, we develop a model that quantitatively links those default probabilities to credit spreads (market prices). The main input quantities to this study are merely industry yield data of different times to maturity and expected default frequencies (EDFs) of Moody’s KMV. The empirical results of this paper clearly indicate that the model can be used to calculate approximate credit spreads (market prices) from EDFs, independent of the time to maturity and the industry sector under consideration. Moreover, the model is effective in an out-of-sample setting, it produces consistent results on the European bond market where data are scarce and can be adequately used to approximate credit spreads on the corporate level.
- …