3,936 research outputs found

    Incorporating Prior Knowledge into Task Decomposition for Large-Scale Patent Classification

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    Abstract. With the adoption of min-max-modular support vector machines (SVMs) to solve large-scale patent classification problems, a novel, simple method for incorporating prior knowledge into task decomposition is proposed and investigated. Two kinds of prior knowledge described in patent texts are considered: time information, and hierarchical structure information. Through experiments using the NTCIR-5 Japanese patent database, patents are found to have time-varying features that considerably affect classification. The experimen-tal results demonstrate that applying min-max modular SVMs with the proposed method gives performance superior to that of conventional SVMs in terms of training time, generalization accuracy, and scalability.

    Robust Sound Event Classification using Deep Neural Networks

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    The automatic recognition of sound events by computers is an important aspect of emerging applications such as automated surveillance, machine hearing and auditory scene understanding. Recent advances in machine learning, as well as in computational models of the human auditory system, have contributed to advances in this increasingly popular research field. Robust sound event classification, the ability to recognise sounds under real-world noisy conditions, is an especially challenging task. Classification methods translated from the speech recognition domain, using features such as mel-frequency cepstral coefficients, have been shown to perform reasonably well for the sound event classification task, although spectrogram-based or auditory image analysis techniques reportedly achieve superior performance in noise. This paper outlines a sound event classification framework that compares auditory image front end features with spectrogram image-based front end features, using support vector machine and deep neural network classifiers. Performance is evaluated on a standard robust classification task in different levels of corrupting noise, and with several system enhancements, and shown to compare very well with current state-of-the-art classification techniques

    Product strategies and survival in schumpeterian environments: evidence from the US security software industry.

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    This paper seeks to explore the drivers of survival in environments characterized by high rates of entry and exit, fragmented market shares, rapid pace of product innovation and proliferation of young ventures. The paper aims to underscore the role played by postentry product strategies, along with their interaction, after carefully controlling for "at entry" factors and demographic conditions. Based on a population of 270 firms that entered the US security software industry between 1989 and 1998, we find evidence that surviving entities are those that are more aggressive in the adoption of versioning and portfolio broadening strategies. In particular, focusing on any one of these two strategies leads to a higher probability of survival as opposed to adopting a mixed strategy.Survival; Versioning; Portfolio broadening; Young ventures; Sotware;

    A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

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    This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers
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