A machine-learning-based post filtering method utilizing block boundary information in HEVC

2019 ◽  
Author(s):  
YUYA YAMAKI ◽  
Yusuke Kameda ◽  
Ichiro Matsuda ◽  
Susumu Itoh
Keyword(s):  
Machine Learning ◽  
Block Boundary ◽  
Filtering Method ◽  
Boundary Information

An Expanded Feature Extraction of E-Mail Header for Spam Recognition

2013 ◽  
Vol 846-847 ◽  
pp. 1672-1675 ◽  
Author(s):  
Yuan Ning Liu ◽  
Ye Han ◽  
Xiao Dong Zhu ◽  
Fei He ◽  
Li Yan Wei
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Spam Filtering ◽  
Feature Sets ◽  
Filtering Method ◽  
The Past ◽  
Machine Learning Methods ◽  
E Mail

Currently a spam filtering method is extracting attributes from e-mail header and using machine learning methods to classify the sample sets. But as time goes on, spammers transform different ways to send spam, which result in a great change of spam's header. So the attributes defined in the past could not deal with this change sufficiently. This paper extracted attributes from all possible forged header fields to expand the feature sets, then used the rough set theory to classify the sample sets. Experiment validated more attributes including in feature sets may lead to greater performance, in terms of higher recall and precision, lower fake recognition than other algorithms.

scholarly journals Machine Learning as a method of adapting offers to the clients

2019 ◽  
Vol 13 ◽  
pp. 267-271
Author(s):  
Jacek Bielecki ◽  
Oskar Ceglarski ◽  
Maria Skublewska-Paszkowska
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Filtering Method ◽  
Advantages And Disadvantages ◽  
Information Filter ◽  
User Ratings ◽  
Research Show

Recommendation systems are class of information filter applications whose main goal is to provide personalized recommendations. The main goal of the research was to compare two ways of creating personalized recommendations. The recommendation system was built on the basis of a content-based cognitive filtering method and on the basis of a collaborative filtering method based on user ratings. The conclusions of the research show the advantages and disadvantages of both methods.

scholarly journals An Effective Pre-Processing Phase for Gene Expression Classification

2018 ◽  
Vol 11 (3) ◽  
pp. 1223
Author(s):  
Choon Sen Seah ◽  
Shahreen Kasim ◽  
Mohd Farhan Md Fudzee ◽  
Mohd Saberi Mohamad ◽  
Rd Rohmat Saedudin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Random Walk ◽  
Bioconductor Package ◽  
Processing Stage ◽  
Missing Value ◽  
Filtering Method ◽  
Initial Stage ◽  
Expression Classification ◽  
Learning Data

A raw dataset prepared by researchers comes with a lot of information. Whether the information is usefull or not, completely depends on the requirement and purposes. In machine learning, data pre-processing is the very initial stage. It is a must to make sure the dataset is totally suitable for the requirement. In significant directed random walk (sDRW), there are three steps in data pre-processing stage. First, we remove unwanted attributes, missing value and proper arrangement, followed by normalization of the expression value and lastly, filtering method is applied. The first two steps are completed by Bioconductor package while the last step is works in sDRW.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning
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