980,096 research outputs found
A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality
Data classification is a major machine learning paradigm, which has been
widely applied to solve a large number of real-world problems. Traditional data
classification techniques consider only physical features (e.g., distance,
similarity, or distribution) of the input data. For this reason, those are
called \textit{low-level} classification. On the other hand, the human (animal)
brain performs both low and high orders of learning and it has a facility in
identifying patterns according to the semantic meaning of the input data. Data
classification that considers not only physical attributes but also the pattern
formation is referred to as \textit{high-level} classification. Several
high-level classification techniques have been developed, which make use of
complex networks to characterize data patterns and have obtained promising
results. In this paper, we propose a pure network-based high-level
classification technique that uses the betweenness centrality measure. We test
this model in nine different real datasets and compare it with other nine
traditional and well-known classification models. The results show us a
competent classification performance
Utilizing Domain Knowledge in End-to-End Audio Processing
End-to-end neural network based approaches to audio modelling are generally
outperformed by models trained on high-level data representations. In this
paper we present preliminary work that shows the feasibility of training the
first layers of a deep convolutional neural network (CNN) model to learn the
commonly-used log-scaled mel-spectrogram transformation. Secondly, we
demonstrate that upon initializing the first layers of an end-to-end CNN
classifier with the learned transformation, convergence and performance on the
ESC-50 environmental sound classification dataset are similar to a CNN-based
model trained on the highly pre-processed log-scaled mel-spectrogram features.Comment: Accepted at the ML4Audio workshop at the NIPS 201
Balance Sheet Interlinkages and Macro-Financial Risk Analysis in the Euro Area
The financial crisis has highlighted the need for models that can identify counterparty risk exposures and shock transmission processes at the systemic level. We use the euro area financial accounts (flow of funds) data to construct a sector-level network of bilateral balance sheet exposures and show how local shocks can propagate throughout the network and affect the balance sheets in other, even seemingly remote, parts of the financial system. We then use the contingent claims approach to extend this accounting-based network of interlinked exposures to risk-based balance sheets which are sensitive to changes in leverage and asset volatility. We conclude that the bilateral cross-sector exposures in the euro area financial system constitute important channels through which local risk exposures and balance sheet dislocations can be transmitted, with the financial intermediaries playing a key role in the processes. High financial leverage and high asset volatility are found to increase a sector’s vulnerability to shocks and contagion. JEL Classification: C22, E01, E21, E44, F36, G01, G12, G14Balance sheet contagion, contingent claims analysis, financial accounts, macro-prudential analysis, network models, systemic risk
- …