Xem 1-20 trên 36 kết quả Logistic regression
  • Reading is known to be an essential task in language learning, but finding the appropriate text for every learner is far from easy. In this context, automatic procedures can support the teacher’s work. Some tools exist for English, but at present there are none for French as a foreign language (FFL). In this paper, we present an original approach to assessing the readability of FFL texts using NLP techniques and extracts from FFL textbooks as our corpus. Two logistic regression models based on lexical and grammatical features are explored and give quite good predictions on new texts. ...

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  • This lecture describes the construction of binary classifiers using a technique called Logistic Regression. The objective is for you to learn: How to apply logistic regression to discriminate between two classes; how to formulate the logistic regression likelihood; how to derive the gradient and Hessian of logistic regression; how to incorporate the gradient vector and Hessian matrix into Newton’s optimization algorithm so as to come up with an algorithm for logistic regression, which we call IRLS.

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  • Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care cung cấp cho các bạn kiến thức về ngành y đề tài: Statistics review 14: Logistic regression...

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Differential item functioning (DIF) analyses of health-related quality of life instruments using logistic regression

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  • Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: Comparison between logistic regression and neural networks to predict death in patients with suspected sepsis in the emergency room...

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  • Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí hóa học đề tài : Differential item functioning (DIF) analyses of health-related quality of life instruments using logistic regression

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  • This lecture describes modular ways of formulating and learning distributed representations of data. The objective is for you to learn: How to specify models such as logistic regression in layers; how to formulate layers and loss criterions; how well formulated local rules results in correct global rules; how back-propagation works; how this manifests itself in Torch.

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  • Hồi qui logistic là một kỹ thuật phân tích hồi qui trong đó biến số phụ thuộc (Y) là một biến số nhị phân (dichotomous – binary variable), theo đó Y thường được mã hoá là 1 và 0 (Y = 1, thành công; Y = 0, thất bại). Biến số độc lập trong hồi qui logistic có thể là biến số rời hoặc liên tục, biến số đơn hoặc đa biến số. Phần này chỉ trình bày những nguyên lý và hiểu biết cơ bản của hồi qui logistic. ...

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  • nalysis requires specifying the link function for the model. The popular link functions used widely include the linear link function, the logistic link function, and the probit link function. However, in many applications, the functional form and the link function might not be known precisely. Therefo......

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  • In an effort to identify some of the most influential algorithms that have been widely used in the data mining community, the IEEE International Conference on Data Mining (ICDM, http://www.cs.uvm.edu/∼icdm/) identified the top 10 algorithms in data mining for presentation at ICDM ’06 in Hong Kong. This book presents these top 10 data mining algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Na¨ıve Bayes, and CART.

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  • As the reader of this book, you are our most important critic and commentator. We value your opinion and want to know what we’re doing right, what we could do better, what areas you’d like to see us publish in, and any other words of wisdom you’re willing to pass our way. As an editor-in-chief for Que Publishing, I welcome your comments. You can email or write me directly to let me know what you did or didn’t like about this book—as well as what we can do to make our books better....

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  • There are many books concerned with statistical theory. This is not one of them. This is a practical book. It is aimed at people who need to understand statistics, but not develop it as a subject. The typical reader might be a postgraduate student in health, life or social science who has no knowledge of statistics, but needs to use quantitative methods in their studies. Students who are engaged in qualitative studies will need to read and understand quantitative studies when they do their literature reviews, this book may be of use to them.

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  • Given a collection of records (training set ) Each record contains a set of attributes, one of the attributes is the class. Find a model for class attribute as a function of the values of other attributes. Goal: previously unseen records should be assigned a class as accurately as possible. A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.

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  • In the first edition of this book, I claimed that humanity could be divided into three groups: (1) those who conduct their own research studies, (2) those who do not formally engage in the research process but nonetheless encounter the results of others’ investigations, and (3) those who are neither “makers” nor “consumers” of research claims. Now, nearly 40 years since I made that statement, I still believe that every person on the face of the Earth can be classified into one of those three groups.

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  • Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups. Understanding Group related documents for browsing, group genes and proteins that have similar functionality, or group stocks with similar price fluctuations Summarization Reduce the size of large data sets

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  • Based on logistic regression, we developed habitat models for the burnet moth Zygaena carniolica and the nymphalid butterfly Coenonympha arcania in Northern Bavaria, Germany. The relation between adult occurrence and habitat parameters - including the influence of the landscape context - was analyzed on 118 sites.

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  • Statistical Prediction Models Bayes' theorem, as presented above, deals with a clinical prediction problem that is unrealistically simple relative to most problems a clinician faces. Prediction models, based on multivariable statistical models, can handle much more complex problems and substantially enhance predictive accuracy for specific situations. Their particular advantage is the ability to take into account many overlapping pieces of information and assign a relative weight to each based on its unique contribution to the prediction in question.

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  • Estimating the risk of in-hospital death is one of the most important considerations for surgeons and patients when they evaluate the option of surgery for lung cancer.The 30 day mortality for lobectomy and pneumonectomy in England from National Lung Cancer Audit is 2.3% and 5.8% respectively. Thoracoscore is currently the largest and most validated global risk score .

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  • A descriptive analysis by frequency counting, central trends measures and dispersion was made for those continuous type variables, as some of the variables inherent to laborers, and environmental and biological measurements. Continuous variables were transformed to normalize them. We also explored possible relationships between some variables and they were crossed by constructing contingency tables. We used the Student's t tests and chi square tests for the comparison of quantitative and categorical variables.

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  • Creates nested clusters Agglomerative clustering algorithms vary in terms of how the proximity of two clusters are computed MIN (single link): susceptible to noise/outliers MAX/GROUP AVERAGE: may not work well with non-globular clusters CURE algorithm tries to handle both problems Often starts with a proximity matrix A type of graph-based algorithm

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