arXiv:1406.1580
Machine learning approach for text and document mining
원문 초록
Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learning (ML) and has received much attention in the last years from both researchers in the academia and industry developers. In this paper, we first categorize the documents using KNN based machine learning approach and then return the most relevant documents.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
FrImCla: A Framework for Image Classification Using Traditional and Transfer Learning Techniques
LLSF - Learning Label Specific Features for Multi-Label Classifcation
ExploreKit: Automatic Feature Generation and Selection
Thumbs up? Sentiment Classification using Machine Learning Techniques
PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches
MUDAS: Mote-scale Unsupervised Domain Adaptation in Multi-label Sound Classification
토론
이 논문과 연결된 포럼 글이 아직 없습니다.