Error-driven learning

<p>In <a href="/facts/Reinforcement_learning/NrgPPS0Q">reinforcement learning</a>, error-driven learning is a method for adjusting a model's (<a href="/facts/Intelligent_agent/8C2Q93Vw">intelligent agent</a>'s) <a href="/facts/Parameter/zFvVFMv9">parameters</a> based on the difference between its output results and the <a href="/facts/Ground_truth/KNNr8X1G">ground truth</a>. These models stand out as they depend on environmental feedback, rather than explicit labels or categories. They are based on the idea that <a href="/facts/Language_acquisition/bwdh0CVG">language acquisition</a> involves the minimization of the <a href="/facts/Mean_squared_prediction_error/bUMwV1eR">prediction error</a> (MPSE). By leveraging these prediction errors, the models consistently refine expectations and decrease computational complexity.  Typically, these <a href="/facts/Algorithm/fnl5NmRt">algorithms</a> are operated by the GeneRec algorithm.
</p><p>Error-driven learning has widespread applications in <a href="/facts/Cognitive_science/8qoggl7r">cognitive sciences</a> and <a href="/facts/Computer_vision/Tl2Yyk66">computer vision</a>. These methods have also found successful application in <a href="/facts/Natural_language_processing/1hjMKsSN">natural language processing</a> (NLP), including areas like <a href="/facts/Part-of-speech_tagging/BYAPmH3r">part-of-speech tagging</a>, <a href="/facts/Parsing/nRKB01S3">parsing</a>, <a href="/facts/Named-entity_recognition/ko6GRQQd">named entity recognition</a> (NER), <a href="/facts/Machine_translation/DGF3NwuI">machine translation</a> (MT), <a href="/facts/Speech_recognition/z7S7Pgk6">speech recognition</a> (SR), and <a href="/facts/Dialogue_system/gbMVkBVf">dialogue systems</a>.
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Error-driven learning open-in-new

Error-driven learning