Classification and Review
It involves reviewing a vast number of documents to identify and separate the relevant ones.
Real-Time Learning
Based on the insights provided by experts, the AI analyzes and categorizes the remaining data.
Fast and Accurate
Through real-time learning, AI analyzes the desired data and prioritizes reviewing highly relevant documents.
How Active Learning Drives Technology Assisted Review (TAR)?
Machine learning techniques with an active learning structure
Active Learning is a specialized machine learning technique where AI interacts with human reviewers to dynamically refine decision-making criteria. This approach minimizes result uncertainty, enhances relevance selection accuracy, and quickly produces precise outcomes by continuously learning from the feedback provided by users.
Methods for Applying Active Learning (AL)
The Active Learning technique allows real-time document analysis and helps users prioritize reviewing the most relevant documents.
Learning with AL = SVM
(Support Vector Machine) type
This is a type of machine learning model known as Binary Classification, which determines which category given data belongs to. It recognizes patterns in the training data and makes decisions based on the boundaries (Support Vectors) between two groups. When these boundaries are unclear, the system actively seeks out criteria to resolve the ambiguity.
Machine Learning Field
Pattern Recognition
Ambiguous Boundary Detection
Effective Learning Ability
