TY - BOOK ID - 33239646 TI - Tree-Based Convolutional Neural Networks : Principles and Applications AU - Mou, Lili. AU - Jin, Zhi. PY - 2018 SN - 9811318700 9811318697 PB - Singapore : Springer Singapore : Imprint: Springer, DB - UniCat KW - Neural networks (Computer science) KW - Artificial neural networks KW - Nets, Neural (Computer science) KW - Networks, Neural (Computer science) KW - Neural nets (Computer science) KW - Artificial intelligence KW - Natural computation KW - Soft computing KW - Artificial intelligence. KW - Data mining. KW - Engineering. KW - Software engineering. KW - Artificial Intelligence. KW - Data Mining and Knowledge Discovery. KW - Computational Intelligence. KW - Software Engineering. KW - Computer software engineering KW - Engineering KW - Construction KW - Industrial arts KW - Technology KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Database searching KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Computational intelligence. KW - Intelligence, Computational UR - https://www.unicat.be/uniCat?func=search&query=sysid:33239646 AB - This book proposes a novel neural architecture, tree-based convolutional neural networks (TBCNNs),for processing tree-structured data. TBCNNsare related to existing convolutional neural networks (CNNs) and recursive neural networks (RNNs), but they combine the merits of both: thanks to their short propagation path, they are as efficient in learning as CNNs; yet they are also as structure-sensitive as RNNs. In this book, readers will also find a comprehensive literature review of related work, detailed descriptions of TBCNNs and their variants, and experiments applied to program analysis and natural language processing tasks. It is also an enjoyable read for all those with a general interest in deep learning. ER -