Active semi-supervised framework with data editing

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

Active and Semi-supervised Data Domain Description

Data domain description techniques aim at deriving concise descriptions of objects belonging to a category of interest. For instance, the support vector domain description (SVDD) learns a hypersphere enclosing the bulk of provided unlabeled data such that points lying outside of the ball are considered anomalous. However, relevant information such as expert and background knowledge remain unuse...

متن کامل

Tri-training and Data Editing Based Semi-supervised Clustering Algorithm

Semi-Supervised clustering algorithms often utilize a seeds set consisting of a small amount of labeled data to initialize cluster centroids, hence improve the clustering performance over whole data set. Both the scale and quality of seeds set directly restrict the performance of semi-supervised clustering algorithm. In this paper, a new algorithm named DE-Tri-training semi-supervised K-means i...

متن کامل

Semi-Supervised Active Clustering with Weak Oracles

Semi-supervised active clustering (SSAC) utilizes the knowledge of a domain expert to cluster data points by interactively making pairwise “same-cluster” queries. However, it is impractical to ask human oracles to answer every pairwise query. In this paper, we study the influence of allowing “not-sure” answers from a weak oracle and propose algorithms to efficiently handle uncertainties. Differ...

متن کامل

Training SpamAssassin with Active Semi-supervised Learning

Most spam filters include some automatic pattern classifiers based on machine learning and pattern recognition techniques. Such classifiers often require a large training set of labeled emails to attain a good discriminant capability between spam and legitimate emails. In addition, they must be frequently updated because of the changes introduced by spammers to their emails to evade spam filter...

متن کامل

Detecting Concept Drift in Data Stream Using Semi-Supervised Classification

Data stream is a sequence of data generated from various information sources at a high speed and high volume. Classifying data streams faces the three challenges of unlimited length, online processing, and concept drift. In related research, to meet the challenge of unlimited stream length, commonly the stream is divided into fixed size windows or gradual forgetting is used. Concept drift refer...

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: Computer Science and Information Systems

سال: 2012

ISSN: 1820-0214,2406-1018

DOI: 10.2298/csis120202045z