Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract#R##N##R##N#Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other ner artificial-intelligence-based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These ner techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article,  first provide an overview of these ner techniques and their use in information science research. To familiarize readers with these techniques,  present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evolution-based genetic algorithms.  discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique.  believe these techniques are promising in their ability to analyze user queries, identify users' information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keyword-based, probabilistic, and knowledge-based techniques. © 1995 John Wiley & Sons, Inc.
['Hsinchun Chen']
I am writing a research paper on a idea Machine learning for information retrieval: neural networks, symbolic learning, and genetic algorithms
[{"from": "human", "value": "I am writing a research paper on a idea Machine learning for information retrieval: neural networks, symbolic learning, and genetic algorithms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract#R##N##R##N#Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to \u201cintelligent\u201d information retrieval and indexing. More recently, information science researchers have turned to other ner artificial-intelligence-based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These ner techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article,  first provide an overview of these ner techniques and their use in information science research. To familiarize readers with these techniques,  present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evolution-based genetic algorithms.  discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique.  believe these techniques are promising in their ability to analyze user queries, identify users' information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keyword-based, probabilistic, and knowledge-based techniques. \u00a9 1995 John Wiley & Sons, Inc."}]