Classification and comparison via neural networks
نویسندگان
چکیده
منابع مشابه
One-class document classification via Neural Networks
Automated document retrieval and classification is of central importance in many contexts; our main motivating goal is the efficient classification and retrieval of ‘‘interests’’ on the internet when only positive information is available. In this paper, we show how a simple feed-forward neural network can be trained to filter documents under these conditions, and that this method seems to be s...
متن کاملOn Effective E-mail Classification via Neural Networks
For addressing the growing problem of junk E-mail on the Internet, this paper proposes an effective E-mail classifying and cleansing method in this paper. Incidentally, E-mail messages can be modelled as semi-structured documents consisting of a set of fields with pre-defined semantics and a number of variable length free-text fields. Our proposed method deals with both fields having pre-define...
متن کاملImplicit Discourse Relation Classification via Multi-Task Neural Networks
Without discourse connectives, classifying implicit discourse relations is a challenging task and a bottleneck for building a practical discourse parser. Previous research usually makes use of one kind of discourse framework such as PDTB or RST to improve the classification performance on discourse relations. Actually, under different discourse annotation frameworks, there exist multiple corpor...
متن کاملClassification of ECG signals using Hermite functions and MLP neural networks
Classification of heart arrhythmia is an important step in developing devices for monitoring the health of individuals. This paper proposes a three module system for classification of electrocardiogram (ECG) beats. These modules are: denoising module, feature extraction module and a classification module. In the first module the stationary wavelet transform (SWF) is used for noise reduction of ...
متن کاملComparison of Inductive Learning of Classification Tasks by Neural Networks*
A number of different data sets are used to compare a variety of neural network training algorithms: backpropagation, quickprop, committees of backpropagation style networks and Cascade Correlation. The results are further compared with a decision tree technique, C4.5, to assess which types of problems are more suited to the different classes of inductive learning algorithms.
متن کاملذخیره 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: Neural Networks
سال: 2019
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2019.06.004