Universitätspublikationen
Refine
Year of publication
Document Type
- Article (13741)
- Part of Periodical (3487)
- Doctoral Thesis (3332)
- Contribution to a Periodical (2163)
- Book (2111)
- Working Paper (1890)
- Preprint (1758)
- Review (1064)
- Report (909)
- Conference Proceeding (703)
Language
- English (17515)
- German (14018)
- Portuguese (231)
- Spanish (123)
- Italian (66)
- French (64)
- Multiple languages (64)
- Turkish (12)
- Ukrainian (10)
- slo (7)
Keywords
- Deutschland (132)
- COVID-19 (98)
- inflammation (96)
- Financial Institutions (92)
- ECB (69)
- Capital Markets Union (67)
- SARS-CoV-2 (64)
- Financial Markets (61)
- Adorno (58)
- Banking Regulation (52)
Institute
- Medizin (6689)
- Präsidium (5136)
- Physik (3598)
- Wirtschaftswissenschaften (2300)
- Gesellschaftswissenschaften (2021)
- Biowissenschaften (1773)
- Frankfurt Institute for Advanced Studies (FIAS) (1671)
- Sustainable Architecture for Finance in Europe (SAFE) (1405)
- Biochemie und Chemie (1400)
- Informatik (1393)
- Center for Financial Studies (CFS) (1266)
- Rechtswissenschaft (1060)
- Neuere Philologien (821)
- House of Finance (HoF) (807)
- Exzellenzcluster Die Herausbildung normativer Ordnungen (735)
- Geschichtswissenschaften (726)
- Geowissenschaften (688)
- Biochemie, Chemie und Pharmazie (674)
- Philosophie (570)
- Kulturwissenschaften (531)
- Psychologie (483)
- E-Finance Lab e.V. (436)
- Universitätsbibliothek (422)
- Institut für Ökologie, Evolution und Diversität (405)
- Institut für Sozialforschung (IFS) (394)
- Senckenbergische Naturforschende Gesellschaft (394)
- Erziehungswissenschaften (377)
- Institut für Wirtschaft, Arbeit, und Kultur (IWAK) (368)
- Pharmazie (335)
- Sprach- und Kulturwissenschaften (312)
- Biodiversität und Klima Forschungszentrum (BiK-F) (292)
- Evangelische Theologie (275)
- Geographie (241)
- Psychologie und Sportwissenschaften (225)
- Geowissenschaften / Geographie (218)
- Sportwissenschaften (217)
- Informatik und Mathematik (207)
- Institute for Monetary and Financial Stability (IMFS) (205)
- Institut für sozial-ökologische Forschung (ISOE) (197)
- Mathematik (193)
- Exzellenzcluster Makromolekulare Komplexe (173)
- MPI für Biophysik (168)
- Sprachwissenschaften (159)
- MPI für Hirnforschung (134)
- Georg-Speyer-Haus (131)
- Sonderforschungsbereiche / Forschungskollegs (126)
- Buchmann Institut für Molekulare Lebenswissenschaften (BMLS) (109)
- Zentrum für Biomolekulare Magnetische Resonanz (BMRZ) (105)
- Ernst Strüngmann Institut (95)
- Zentrum für Arzneimittelforschung, Entwicklung und Sicherheit (ZAFES) (93)
- Philosophie und Geschichtswissenschaften (80)
- Institute for Law and Finance (ILF) (68)
- Katholische Theologie (64)
- Cornelia Goethe Centrum für Frauenstudien und die Erforschung der Geschlechterverhältnisse (CGC) (59)
- Deutsches Institut für Internationale Pädagogische Forschung (DIPF) (55)
- Foundation of Law and Finance (51)
- Hessische Stiftung für Friedens- und Konfliktforschung (HSFK) (48)
- MPI für empirische Ästhetik (44)
- Interdisziplinäres Zentrum für Ostasienstudien (IZO) (40)
- ELEMENTS (37)
- Gleichstellungsbüro (36)
- Fachübergreifend (35)
- Frobenius Institut (33)
- Europäische Akademie der Arbeit in der Universität Frankfurt am Main (32)
- Zentrum für Weiterbildung (31)
- Starker Start ins Studium: Qualitätspakt Lehre (30)
- Extern (29)
- Hochschulrechenzentrum (27)
- Zentrum für Interdisziplinäre Afrikaforschung (ZIAF) (26)
- LOEWE-Schwerpunkt Außergerichtliche und gerichtliche Konfliktlösung (25)
- studiumdigitale (25)
- Sigmund-Freud Institut – Forschungsinstitut fur Psychoanalyse und ihre Anwendungen (19)
- Universität des 3. Lebensalters e.V. (19)
- Akademie für Bildungsforschung und Lehrerbildung (bisher: Zentrum für Lehrerbildung und Schul- und Unterrichtsforschung) (18)
- Exzellenzcluster Herz-Lungen-System (18)
- Center for Membrane Proteomics (CMP) (17)
- Museum Giersch der Goethe Universität (17)
- Forschungskolleg Humanwissenschaften (16)
- Centre for Drug Research (15)
- Zentrum für Nordamerika-Forschung (ZENAF) (15)
- Center for Scientific Computing (CSC) (11)
- Interdisziplinäres Zentrum für Neurowissenschaften Frankfurt (IZNF) (9)
- keine Angabe Institut (9)
- LOEWE-Schwerpunkt für Integrative Pilzforschung (8)
- Forschungszentrum Historische Geisteswissenschaften (FHG) (7)
- Fritz Bauer Institut (7)
- Helmholtz International Center for FAIR (7)
- DFG-Forschergruppen (6)
- Goethe-Zentrum für Wissenschaftliches Rechnen (G-CSC) (6)
- Zentrum für Hochschulsport (ZfH) (6)
- Internationales Studienzentrum (4)
- Frankfurt MathFinance Institute (FMFI) (3)
- Institut für Religionsphilosophische Forschung (3)
- Schreibzentrum (3)
- Zentrale Einrichtung (3)
- (2)
- Akademie für Bildungsforschung und Lehrkräftebildung (2)
- Institut für Bienenkunde (2)
- Wilhelm-Merton-Zentrum (2)
- Diagnostic Center of Acute Leukemia (1)
- Exzellenzcluster (1)
- Fachübergreifende Einrichtungen (1)
- Forschungscluster (1)
- GRADE - Goethe Research Academy for Early Career Researchers (1)
- Katholische Hochschulgemeinde (KHG) (1)
- SFB 268 (1)
- Zentrum zur Erforschung der Frühen Neuzeit (Renaissance-Institut) (1)
- keine Angabe Fachbereich (1)
In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. In this paper, for the small, important example of inflammation modeling a network is constructed and different learning algorithms are proposed. It turned out that due to the nonlinear dynamics evolutionary approaches are necessary to fit the parameters for sparse, given data. Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence - ICTAI 2003
The early prediction of mortality is one of the unresolved tasks in intensive care medicine. This contribution models medical symptoms as observations cased by transitions between hidden markov states. Learning the underlying state transition probabilities results in a prediction probability success of about 91%. The results are discussed and put in relation to the model used. Finally, the rationales for using the model are reflected: Are there states in the septic shock data?
In intensive care units physicians are aware of a high lethality rate of septic shock patients. In this contribution we present typical problems and results of a retrospective, data driven analysis based on two neural network methods applied on the data of two clinical studies. Our approach includes necessary steps of data mining, i.e. building up a data base, cleaning and preprocessing the data and finally choosing an adequate analysis for the medical patient data. We chose two architectures based on supervised neural networks. The patient data is classified into two classes (survived and deceased) by a diagnosis based either on the black-box approach of a growing RBF network and otherwise on a second network which can be used to explain its diagnosis by human-understandable diagnostic rules. The advantages and drawbacks of these classification methods for an early warning system are discussed.
In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. In this paper, for the small, important example of inflammation modeling a network is constructed and different learning algorithms are proposed. It turned out that due to the nonlinear dynamics evolutionary approaches are necessary to fit the parameters for sparse, given data. Keywords: model parameter adaption, septic shock. coupled differential equations, genetic algorithm.
The selection of features for classification, clustering and approximation is an important task in pattern recognition, data mining and soft computing. For real-valued features, this contribution shows how feature selection for a high number of features can be implemented using mutual in-formation. Especially, the common problem for mutual information computation of computing joint probabilities for many dimensions using only a few samples is treated by using the Rènyi mutual information of order two as computational base. For this, the Grassberger-Takens corre-lation integral is used which was developed for estimating probability densities in chaos theory. Additionally, an adaptive procedure for computing the hypercube size is introduced and for real world applications, the treatment of missing values is included. The computation procedure is accelerated by exploiting the ranking of the set of real feature values especially for the example of time series. As example, a small blackbox-glassbox example shows how the relevant features and their time lags are determined in the time series even if the input feature time series determine nonlinearly the output. A more realistic example from chemical industry shows that this enables a better ap-proximation of the input-output mapping than the best neural network approach developed for an international contest. By the computationally efficient implementation, mutual information becomes an attractive tool for feature selection even for a high number of real-valued features.
In its first part, this contribution reviews shortly the application of neural network methods to medical problems and characterizes its advantages and problems in the context of the medical background. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic systems. Then, paradigm of neural networks is shortly introduced and the main problems of medical data base and the basic approaches for training and testing a network by medical data are described. Additionally, the problem of interfacing the network and its result is given and the neuro-fuzzy approach is presented. Finally, as case study of neural rule based diagnosis septic shock diagnosis is described, on one hand by a growing neural network and on the other hand by a rule based system. Keywords: Statistical Classification, Adaptive Prediction, Neural Networks, Neurofuzzy, Medical Systems
In contrast to the symbolic approach, neural networks seldom are designed to explain what they have learned. This is a major obstacle for its use in everyday life. With the appearance of neuro-fuzzy systems which use vague, human-like categories the situation has changed. Based on the well-known mechanisms of learning for RBF networks, a special neuro-fuzzy interface is proposed in this paper. It is especially useful in medical applications, using the notation and habits of physicians and other medically trained people. As an example, a liver disease diagnosis system is presented.
The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: Since only one financial transaction of a thousand is invalid no prediction success less than 99.9% is acceptable. Due to these credit card transaction proportions complete new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate.
This paper describes the use of a Radial Basis Function (RBF) neural network in the approximation of process parameters for the extrusion of a rubber profile in tyre production. After introducing the rubber industry problem, the RBF network model and the RBF net learning algorithm are developed, which uses a growing number of RBF units to compensate the approximation error up to the desired error limit. Its performance is shown for simple analytic examples. Then the paper describes the modelling of the industrial problem. Simulations show good results, even when using only a few training samples. The paper is concluded by a discussion of possible systematic error influences, improvements and potential generalisation benefits. Keywords: Adaptive process control; Parameter estimation; RBF-nets; Rubber extrusion
Diese Arbeit plädiert für eine rationale Behandlung von Patientendaten und untersucht dazu die Analyse der Daten mit Hilfe neuronale Netze etwas näher. Erfolgreiche Beispielanwendungen zeigen, daß die menschlichen Diagnosefähigkeiten deutlich schlechter sind als neuronale Diagnosesysteme. Für das Beispiel der neueren Architektur mit RBF-Netzen wird die Funktionalität näher erläutert und gezeigt, wie menschliche und neuronale Expertise miteinander gekoppelt werden kann. Der Ausblick deutet Anwendungen und Praxisproblematik derartiger Systeme an.
The encoding of images by semantic entities is still an unresolved task. This paper proposes the encoding of images by only a few important components or image primitives. Classically, this can be done by the Principal Component Analysis (PCA). Recently, the Independent Component Analysis (ICA) has found strong interest in the signal processing and neural network community. Using this as pattern primitives we aim for source patterns with the highest occurrence probability or highest information. For the example of a synthetic image composed by characters this idea selects the salient ones. For natural images it does not lead to an acceptable reproduction error since no a-priori probabilities can be computed. Combining the traditional principal component criteria of PCA with the independence property of ICA we obtain a better encoding. It turns out that the Independent Principal Components (IPC) in contrast to the Principal Independent Components (PIC) implement the classical demand of Shannon’s rate distortion theory.
This paper proposes a new approach for the encoding of images by only a few important components. Classically, this is done by the Principal Component Analysis (PCA). Recently, the Independent Component Analysis (ICA) has found strong interest in the neural network community. Applied to images, we aim for the most important source patterns with the highest occurrence probability or highest information called principal independent components (PIC). For the example of a synthetic image composed by characters this idea selects the salient ones. For natural images it does not lead to an acceptable reproduction error since no a-priori probabilities can be computed. Combining the traditional principal component criteria of PCA with the independence property of ICA we obtain a better encoding. It turns out that this definition of PIC implements the classical demand of Shannon’s rate distortion theory.
This paper describes the problems and an adaptive solution for process control in rubber industry. We show that the human and economical benefits of an adaptive solution for the approximation of process parameters are very attractive. The modeling of the industrial problem is done by the means of artificial neural networks. For the example of the extrusion of a rubber profile in tire production our method shows good results even using only a few training samples.
In this paper we regard first the situation where parallel channels are disturbed by noise. With the goal of maximal information conservation we deduce the conditions for a transform which "immunizes" the channels against noise influence before the signals are used in later operations. It shows up that the signals have to be decorrelated and normalized by the filter which corresponds for the case of one channel to the classical result of Shannon. Additional simulations for image encoding and decoding show that this constitutes an efficient approach for noise suppression. Furthermore, by a corresponding objective function we deduce the stochastic and deterministic learning rules for a neural network that implements the data orthonormalization. In comparison with other already existing normalization networks our network shows approximately the same in the stochastic case but, by its generic deduction ensures the convergence and enables the use as independent building block in other contexts, e.g. whitening for independent component analysis. Keywords: information conservation, whitening filter, data orthonormalization network, image encoding, noise suppression.
Im Zeitraum 1. 11. 1993 bis 30. 3. 1997 wurden 1149 allgemeinchirurgische Intensivpatienten prospektiv erfaßt, von denen 114 die Kriterien des septischen Schocks erfüllten. Die Letalität der Patienten mit einem septischen Schock betrug 47,3%. Nach Training eines neuronalen Netzes mit 91 (von insgesamt n = 114) Patienten ergab die Testung bei den verbleibenden 23 Patienten bei der Berücksichtigung von Parameterveränderungen vom 1. auf den 2. Tag des septischen Schocks folgendes Ergebnis: Alle 10 verstorbenen Patienten wurden korrekt als nicht überlebend vorhergesagt, von den 13 Überlebenden wurden 12 korrekt als überlebend vorhergesagt (Sensitivität 100%; Spezifität 92,3%).
This paper describes the use of a radial basis function (RBF) neural network. It approximates the process parameters for the extrusion of a rubber profile used in tyre production. After introducing the problem, we describe the RBF net algorithm and the modeling of the industrial problem. The algorithm shows good results even using only a few training samples. It turns out that the „curse of dimensions“ plays an important role in the model. The paper concludes by a discussion of possible systematic error influences and improvements.
The paper focuses on the division of the sensor field into subsets of sensor events and proposes the linear transformation with the smallest achievable error for reproduction: the transform coding approach using the principal component analysis (PCA). For the implementation of the PCA, this paper introduces a new symmetrical, lateral inhibited neural network model, proposes an objective function for it and deduces the corresponding learning rules. The necessary conditions for the learning rate and the inhibition parameter for balancing the crosscorrelations vs. the autocorrelations are computed. The simulation reveals that an increasing inhibition can speed up the convergence process in the beginning slightly. In the remaining paper, the application of the network in picture encoding is discussed. Here, the use of non-completely connected networks for the self-organized formation of templates in cellular neural networks is shown. It turns out that the self-organizing Kohonen map is just the non-linear, first order approximation of a general self-organizing scheme. Hereby, the classical transform picture coding is changed to a parallel, local model of linear transformation by locally changing sets of self-organized eigenvector projections with overlapping input receptive fields. This approach favors an effective, cheap implementation of sensor encoding directly on the sensor chip. Keywords: Transform coding, Principal component analysis, Lateral inhibited network, Cellular neural network, Kohonen map, Self-organized eigenvector jets.
After a short introduction into traditional image transform coding, multirate systems and multiscale signal coding the paper focuses on the subject of image encoding by a neural network. Taking also noise into account a network model is proposed which not only learns the optimal localized basis functions for the transform but also learns to implement a whitening filter by multi-resolution encoding. A simulation showing the multi-resolution capabilitys concludes the contribution.
We present a framework for the self-organized formation of high level learning by a statistical preprocessing of features. The paper focuses first on the formation of the features in the context of layers of feature processing units as a kind of resource-restricted associative multiresolution learning We clame that such an architecture must reach maturity by basic statistical proportions, optimizing the information processing capabilities of each layer. The final symbolic output is learned by pure association of features of different levels and kind of sensorial input. Finally, we also show that common error-correction learning for motor skills can be accomplished also by non-specific associative learning. Keywords: feedforward network layers, maximal information gain, restricted Hebbian learning, cellular neural nets, evolutionary associative learning