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Auflistung Künstliche Intelligenz 26(4) - November 2012 nach Titel
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- ZeitschriftenartikelAdaptive Dissimilarity Measures, Dimension Reduction and Visualization (University of Groningen)(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Bunte, Kerstin
- ZeitschriftenartikelAdmire LVQ—Adaptive Distance Measures in Relevance Learning Vector Quantization(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Biehl, MichaelThe extension of Learning Vector Quantization by Matrix Relevance Learning is presented and discussed. The basic concept, essential properties, and several modifications of the scheme are outlined. A particularly successful application in the context of tumor classification highlights the usefulness and interpretability of the method in practical contexts. The development and putting forward of Matrix Relevance Learning Vector Quantization was, to a large extent, pursued in the frame of the project Adaptive Distance Measures in Relevance Learning Vector Quantization—Admire LVQ, funded through the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) under project code 612.066.620, from 2007 to 2011.
- ZeitschriftenartikelChallenges in Neural Computation(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Hammer, BarbaraThis contribution contains a short history of neural computation and an overview about the major learning paradigms and neural architectures used today.
- ZeitschriftenartikelConnecting Question Answering and Conversational Agents(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Waltinger, Ulli; Breuing, Alexa; Wachsmuth, IpkeResearch results in the field of Question Answering (QA) have shown that the classification of natural language questions significantly contributes to the accuracy of the generated answers. In this paper we present an approach which extends the prevalent question classification techniques by additionally considering further contextual information provided by the questions. Thereby we focus on improving the conversational abilities of existing interactive interfaces by enhancing their underlying QA systems in terms of response time and correctness. As a result, we are able to introduce a method based on a tripartite contextualization. First, we present a comprehensive question classification experiment based on machine learning using two different datasets and various feature sets for the German language. Second, we propose a method for detecting the focus chunk of a given question, that is, for identifying which part of the question is fundamentally relevant to the answer and which part refers to a specification of it. Third, we investigate how to identify and label the topic of a given question by means of a human-judgment experiment. We show that the resulting contextualization method contributes to an improvement of existing question answering systems and enhances their application within interactive scenarios.
- ZeitschriftenartikelDeep Learning(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Schulz, Hannes; Behnke, SvenHierarchical neural networks for object recognition have a long history. In recent years, novel methods for incrementally learning a hierarchy of features from unlabeled inputs were proposed as good starting point for supervised training. These deep learning methods—together with the advances of parallel computers—made it possible to successfully attack problems that were not practical before, in terms of depth and input size. In this article, we introduce the reader to the basic concepts of deep learning, discuss selected methods in detail, and present application examples from computer vision and speech recognition.
- ZeitschriftenartikelHow Rich Motor Skills Empower Robots at Last: Insights and Progress of the AMARSi Project(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Soltoggio, Andrea; Steil, Jochen J.Flexible, robust, precise, adaptive, compliant and safe: these are some of the qualities robots must have to interact safely and productively with humans. Yet robots are still nowadays perceived as too rigid, clumsy and not sufficiently adaptive to work efficiently in interaction with people. The AMARSi Project endeavors to design and implement rich motor skills, unique flexibility, compliance and state-of-the-art learning in robots. Inspired by human-recorded motion and learning behavior, similarly versatile and constantly adaptive movements and skills endow robots with singularly human-like motor dynamics and learning. The AMARSi challenge is to integrate novel biological notions, advanced learning algorithms and cutting-edge compliant mechanics in the design of fully-fledged humanoid and quadruped robots with an unprecedented aptitude for integrating in our environments.
- ZeitschriftenartikelInterview with Helge Ritter(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Mokbel, Bassam
- ZeitschriftenartikelModell und Gegenstand – untrennbar miteinander verbunden(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Ludwig, Bernd
- ZeitschriftenartikelNeural Learning of Cognitive Control(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Hamker, Fred H.Our goal is to develop cognitive agents based on neuroscientific evidence. The efficiency of cognitive behavior depends on its capacity to select, represent and manipulate sufficient knowledge of the environment to achieve its goals. We designed a biologically motivated model of basal ganglia and particularly the prefrontal cortex and here review its foundations of neural learning and summarize our obtained results.
- ZeitschriftenartikelNeural Networks for Complex Data(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Cottrell, Marie; Olteanu, Madalina; Rossi, Fabrice; Rynkiewicz, Joseph; Villa-Vialaneix, NathalieArtificial neural networks are simple and efficient machine learning tools. Defined originally in the traditional setting of simple vector data, neural network models have evolved to address more and more difficulties of complex real world problems, ranging from time evolving data to sophisticated data structures such as graphs and functions. This paper summarizes advances on those themes from the last decade, with a focus on results obtained by members of the SAMM team of Université Paris 1.