1. Naïve Bayes

    0 Comments Leave a Comment

    1-15 of 58 1 2 3 4 »
    1. Mentioned In 58 Articles

    2. Understanding patient satisfaction with received healthcare services: A natural language processing approach.

      Understanding patient satisfaction with received healthcare services: A natural language processing approach.
      Understanding patient satisfaction with received healthcare services: A natural language processing approach. AMIA Annu Symp Proc. 2016;2016:524-533 Authors: Doing-Harris K, Mowery DL, Daniels C, Chapman WW, Conway M Abstract Important information is encoded in free-text patient comments. We determine the most common topics in patient comments, design automatic topic classifiers, identify comments ' sentiment, and find new topics in negative comments.
      Read Full Article
    3. Markov Chain Process -1 by jinkai89

      Markov Chain Process -1 by jinkai89
      aki003iitr I am a data science and analytics professional with a degree in engineering . I am well versed with machine learning techniques. I am also a Mathematics expert. A diligent, zealous and disciplined individual who thrives in pressure situations and likes to work in a challenging environment.An insatiable intellectual curiosity, and the ability to mine hidden information located within large sets of structured, semi-structured and unstructured data.
      Read Full Article
    4. Extracting Information from Electronic Medical Records to Identify the Obesity Status of a Patient Based on Comorbidities and Bodyweight Measures.

      Extracting Information from Electronic Medical Records to Identify the Obesity Status of a Patient Based on Comorbidities and Bodyweight Measures.
      ...onhierarchical one. In general, our results show that Support Vector Machine obtains better performances than Naïve Bayes for both classification problems. We also observed that bigram representation improves performance...
      Read Full Article
    5. From 0 to 1: Machine Learning, Nlp & Python-Cut to the Chase

      From 0 to 1: Machine Learning, Nlp & Python-Cut to the Chase
      Kategoria: Poradniki (Tutorials) | Autor: sergio090588 | Wyświetle From 0 to 1: Machine Learning, NLP Python-Cut to the Chase .MP4, AVC, 1000 kbps, 1280x720 | English, AAC, 64 kbps, 2 Ch | 7 hours | 2.87 GB Instructors: Loony Corn Team A down-to-earth, shy but confident take on machine learning techniques that you can put to work todayThe course is down-to-earth : it makes everything as simple as possible - but not simplerThe course is shy ...
      Read Full Article
    6. 1-15 of 58 1 2 3 4 »
  1. Categories

    1. Default:

      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD
  2. About Naïve Bayes

    A naive Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be independent feature model.

    Depending on the precise nature of the probability model, naive Bayes classifiers can be trained very efficiently in a supervised learning setting. In many practical applications, parameter estimation for naive Bayes models uses the method of maximum likelihood; in other words, one can work with the naive Bayes model without believing in Bayesian probability or using any Bayesian methods.

    In spite of their naive design and apparently over-simplified assumptions, naive Bayes classifiers often work much better in many complex real-world situations than one might expect. Recently, careful analysis of the Bayesian classification problem has shown that there are some theoretical reasons for the apparently unreasonable efficacy of naive Bayes classifiers( Zhang04). An advantage of the Naive Bayes classifier is that it requires a small amount of training data to estimate the parameters (means and variances of the variables) necessary for classification. Because independent variables are assumed, only the variances of the variables for each class need to be determined and not the entire covariance matrix."