Suchergebnisse

Financial support from the Spanish Ministry of Universities. "Disruptive group decision making systems in fuzzy context: Applications in smart energy and people analytics" (PID2019-103880RB-I00). Main Investigator: Enrique Herrera Viedma, and Junta de Andalucia. "Excellence Groups" (P12.SEJ.2463) and Junta de Andalucia (SEJ340) are gratefully acknowledged. Research partially supported by the "Maria de Maeztu" Excellence Unit IMAG, reference CEX2020001105-M, funded by MCIN/AEI/10.13039/501100011033/. ; ANNs succeed in several tasks for real scenarios due to their high learning abilities. This paper focuses on theoretical aspects of ANNs to enhance the capacity of implementing those modifications that make ANNs absorb the defining features of each scenario. This work may be also encompassed within the trend devoted to providing mathematical explanations of ANN performance, with special attention to activation functions. The base algorithm has been mathematically decoded to analyse the required features of activation functions regarding their impact on the training process and on the applicability of the Universal Approximation Theorem. Particularly, significant new results to identify those activation functions which undergo some usual failings (gradient preserving) are presented here. This is the first paper—to the best of the author's knowledge—that stresses the role of injectivity for activation functions, which has received scant attention in literature but has great incidence on the ANN performance. In this line, a characterization of injective activation functions has been provided related to monotonic functions which satisfy the classical contractive condition as a particular case of Lipschitz functions. A summary table on these is also provided, targeted at documenting how to select the best activation function for each situation. ; Spanish Government PID2019-103880RB-I00 ; Junta de Andalucía P12.SEJ.2463 SEJ340 ; "Maria de Maeztu" Excellence Unit IMAG - MCIN/AEI CEX2020001105-M

Neural Networks are an emerging computer technology which is attempting to mimic a naturally intelligent network, the human brain. These networks have the potential for performing far beyond the capabilities of the conventional supercomputer. Neural networks teach themselves as they work. This is unlike Artificial Intelligence systems, which are not capable of teaching themselves. Neural networks have very strong potential for becoming the way of the future in the computer industry. This technology is already being used by certain industries as well as the government. For instance, neural networks are being used by some brokerage firms to try to learn patterns in the stock and commodities markets. And the government is researching its potential uses for defense purposes. The purpose of this paper is to determine whether a business specializing in this new technology would be profitable. Encompassed within this analysis will be a detailed description of what must be done to establish this business.

Although neural networks do offer a few advantages over some other nonlinear methods, in certain situations these advantages are difficult to utilize. However, many neural network applications in the social sciences are flawed in ways that obfuscate such effects. In this article, the neural network methodology is reviewed, some common flaws are pointed out, and a rather commonplace data set—dealing with school delinquency—is analyzed for illustrative purposes.

Abstract
This article explores the domain of legal analysis and its methodologies, emphasising the significance of generalisation in legal systems. It discusses the process of generalisation in relation to legal concepts and the development of ideal concepts that form the foundation of law. The article examines the role of logical induction and its similarities with semantic generalisation, highlighting their importance in legal decision-making. It also critiques the formal-deductive approach in legal practice and advocates for more adaptable models, incorporating fuzzy logic, non-monotonic defeasible reasoning, and artificial intelligence. The potential application of neural networks, specifically deep learning algorithms, in legal theory is also discussed. The article discusses how neural networks encode legal knowledge in their synaptic connections, while the syllogistic model condenses legal information into axioms. The article also highlights how neural networks assimilate novel experiences and exhibit evolutionary progression, unlike the deductive model of law. Additionally, the article examines the historical and theoretical foundations of jurisprudence that align with the basic principles of neural networks. It delves into the statistical analysis of legal phenomena and theories that view legal development as an evolutionary process. The article then explores Friedrich Hayek's theory of law as an autonomous self-organising system and its compatibility with neural network models. It concludes by discussing the implications of Hayek's theory on the role of a lawyer and the precision of neural networks.

Comunicació presentada a la 2016 Conference of the North American Chapter of the Association for Computational Linguistics, celebrada a San Diego (CA, EUA) els dies 12 a 17 de juny 2016. ; We introduce recurrent neural network grammars,/nprobabilistic models of sentences with/nexplicit phrase structure. We explain efficient/ninference procedures that allow application to/nboth parsing and language modeling. Experiments/nshow that they provide better parsing in/nEnglish than any single previously published/nsupervised generative model and better language/nmodeling than state-of-the-art sequential/nRNNs in English and Chinese. ; This work was sponsored in part by the Defense/nAdvanced Research Projects Agency (DARPA)/nInformation Innovation Office (I2O) under the/nLow Resource Languages for Emergent Incidents/n(LORELEI) program issued by DARPA/I2O under/nContract No. HR0011-15-C-0114; it was also supported/nin part by Contract No. W911NF-15-1-0543/nwith the DARPA and the Army Research Office/n(ARO). Approved for public release, distribution/nunlimited. The views expressed are those of the authors/nand do not reflect the official policy or position/nof the Department of Defense or the U.S. Government./nMiguel Ballesteros was supported by the/nEuropean Commission under the contract numbers/nFP7-ICT-610411 (project MULTISENSOR) and/nH2020-RIA-645012 (project KRISTINA).