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Register a new userSemantic Classification and Learning Using a Linear Tranformation Model in a Probabilistic Type Theory with Records
التصنيف والتعلم الدلالي باستخدام نموذج تحول خطي في نظرية نوع الاحتمالية مع السجلات
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بدءا من حساب موجود للتصنيف الدلالي والتعلم من التفاعل المصنوع في نظرية النوع الاحتمالية مع السجلات، يشمل الاستدلال بايزي والتعلم بنكهة متكررة، نلاحظ بعض المشاكل في هذا الحساب وتقديم حساب بديل للتعلم التصنيف الذي يعالج الملاحظمشاكل.الحساب المقترح هو أيضا بايزيا على نطاق واسع في الطبيعة ولكن بدلا من ذلك يستخدم نموذج تحويل خطي للتصنيف والتعلم.
Starting from an existing account of semantic classification and learning from interaction formulated in a Probabilistic Type Theory with Records, encompassing Bayesian inference and learning with a frequentist flavour, we observe some problems with this account and provide an alternative account of classification learning that addresses the observed problems. The proposed account is also broadly Bayesian in nature but instead uses a linear transformation model for classification and learning.
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We propose a probabilistic account of semantic inference and classification formulated in terms of probabilistic type theory with records, building on Cooper et. al. (2014) and Cooper et. al. (2015). We suggest probabilistic type theoretic formulatio
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