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Additive smoothing
Sequence Models
Introduction to Machine Learning Lecture 3
Additive Smoothing for Relevance-Based Language Modelling of Recommender Systems
Smoothing Parameter and Model Selection for General Smooth Models
Introduction to Naivebayes Package
Adding Improvements to Multinomial Naive Bayes for Increasing the Accuracy of Aggressive Tweets Classification
Efficient Learning of Smooth Probability Functions from Bernoulli Tests With
Bias/Variance Tradeoff
Package 'Naivebayes'
Language Modeling and Probability
How to Count Thumb-Ups and Thumb-Downs: User-Rating Based Ranking of Items from an Axiomatic Perspective
Language Models
A Comparison of Smoothing Techniques for Bilingual Lexicon Extraction from Comparable Corpora
Axiomatic Analysis of Smoothing Methods in Language Models for Pseudo-Relevance Feedback
Modeling MOOC Student Behavior with Two-Layer Hidden Markov Models
Maximum Likelihood and Smoothing
Near-Optimal Smoothing of Structured Conditional Probability Matrices
Priors for Bayesian Adaptive Spline Smoothing
Top View
Naive Bayes and Text Classification I-Introduction and Theory
Hidden Markov Model and Naive Bayes Relationship
Laplace's Rule of Succession in Information Geometry Yann Ollivier
Machine Learning for Language Modelling Part 2: N-Gram Smoothing
An Empirical Study of Smoothing Techniques for Language Modeling
Smoothing Parameter Estimation Framework for IBM Word Alignment
Similarities and Differences Between the Statistical Models for Speech Recognition and Parts-Of-Speech Tagging
Bayesian Inference and Naive Bayes
Finding Association Rules by Direct Estimation of Likelihood Ratios