Titlebook: Artificial Intelligence XXXIV; 37th SGAI Internatio Max Bramer,Miltos Petridis Conference proceedings 2017 Springer International Publishin

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遭遇

Masked Conditional Neural Networks for Environmental Sound Classification the Masked ConditionaL Neural Network (MCLNN) induces the network to learn in frequency bands by embedding a filterbank-like sparseness over the network’s links using a binary mask. Additionally, the masking automates the exploration of different feature combinations concurrently analogous to handc

Ingest

Ensembles of Recurrent Neural Networks for Robust Time Series Forecasting fits often involves complex and time consuming tasks such as extensive data preprocessing, designing hybrid models, or heavy parameter optimization. Long Short-Term Memory (LSTM), a variant of recurrent neural networks (RNNs), provide state of the art forecasting performance without prior assumptio

BILL

A Blackboard Based Hybrid Multi-Agent System for Improving Classification Accuracy Using Reinforcemed for tackling complex data classification problems. A trust metric for evaluating agent’s performance and expertise based on Q-learning and employing different voting processes is formulated. Specifically, multiple heterogeneous machine learning agents, are devised to form the expertise group for t

Engaging

Programming Without Program or How to Program in Natural Language Utterancesty of numerous speech-to-text services, gives access to practical voice recognition. Enguage?is an open, programmable speech understanding engine, prototyped in Java, which is built into an app on Google Play, acting entirely as its user interface. Thus, devices can be instructed, and present result

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Campaign

Towards a Deep Reinforcement Learning Approach for Tower Line Warsly playing and winning relatively advanced computer games. There is undoubtedly an anticipation that Deep Reinforcement Learning will play a major role when the first AI masters the complicated game plays needed to beat a professional Real-Time Strategy game player. For this to be possible, there ne

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Improving Modular Classification Rule Induction with G-Prism Using Dynamic Rule Term Boundaries based classifiers. Prism classifiers achieve a similar classification accuracy compared with decision trees, but tend to overfit less, especially if there is noise in the data. This paper describes the development of a new member of the Prism family, the G-Prism classifier, which improves the class

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