Development of Supervised Learning Predictive Models for Highly Non-linear Biological, Biomedical, and General Datasets

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

General Bounds for Predictive Errors in Supervised Learning

Within a Bayesian framework, we calculate general upper and lower bounds for a cumulative entropic error, which measures the success in the supervised learning of an unknown rule from examples. This performance measure is equivalent to the mutual information between the data and the parameter that specifies the rule to be learnt. Both bounds match asymptotically, when the number m of observed d...

متن کامل

Predictive Efficiency for Simple Non-linear Models*

This paper demonstrates the use of exact predictive likelihood functions for simple non-linear models. A measure of predictive efficiency based on the concept of expected information loss is introduced as a way of comparing alternative prediction functions. It is shown that the predictive likelihood function minimizes expected information loss over a wide class of potential prediction functions...

متن کامل

Fast SFFS-Based Algorithm for Feature Selection in Biomedical Datasets

Biomedical datasets usually include a large number of features relative to the number of samples. However, some data dimensions may be less relevant or even irrelevant to the output class. Selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. To this end, this paper presents a hybrid method of filter and wr...

متن کامل

Semi-supervised learning for biomedical information extraction

s Seen 434 76.32 95.4 84.8 Unseen 195 34.54 73.63 47.02 Overall 629 63.43 90.89 74.72 Full papers Seen 801 74.78 94.48 83.48 Unseen 1,179 53.6 86.58 66.21 Overall 1,980 62.17 90.25 73.62 Table 2.6: Evaluation of the CRF+syntax system trained on the automatically annotated abstracts and evaluated on the abstracts and the full papers dataset. found in the dictionary from FlyBase were not found in...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Frontiers in Molecular Biosciences

سال: 2020

ISSN: 2296-889X

DOI: 10.3389/fmolb.2020.00013