Bootstrapping Deep Learning With Machine Learning Labeling
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Solution Overview
Problem
Existing deep learning systems require large amounts of manually labeled training data, which is resource-intensive and prone to human error, and they often struggle to accommodate new classes or classification types efficiently.
Innovation Solution
A method that bootstraps a machine learning system to generate training data for a deep learning system, using a machine learning subsystem to label live unlabeled data elements and generate sub-models, which are then used to train the deep learning subsystem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually labeled training data is used for deep learning systems, then the system can achieve accurate classification, but the process becomes resource-intensive and prone to human error
Solution Approach 1:
The system enables unlabeled data elements to be automatically labeled through self-service mechanisms. The bootstrapped machine learning system generates predictions that serve as labels, eliminating the need for manual human labeling while maintaining classification accuracy.
Solution Approach 2:
The system performs preliminary labeling actions by pre-training machine learning models on available data before the deep learning system is fully operational. This preliminary action creates a foundation of labeled data that accelerates subsequent training and reduces manual intervention requirements.
2Adaptability or versatility
If traditional machine learning models with manual feature engineering are used, then the system can be customized for specific applications, but creating additional classes becomes resource intensive
Solution Approach 1:
The bootstrapped machine learning system serves multiple functions: it acts as both a standalone classifier and as a training data generator for deep learning systems. This universal approach eliminates the need to rebuild feature engineering pipelines when adding new classes, as the same system adapts to generate labels for any classification task.
Solution Approach 2:
The system replaces manual mechanical feature engineering processes with automated machine learning models. Instead of manually extracting and engineering features for each new class, the bootstrapped system automatically learns relevant features and generates labels, substituting human effort with automated computational processes.
3Ease of operation
If linear classification models are used, then the model can interpret feature relationships, but it struggles to handle anomalous or outlying values effectively
Solution Approach 1:
The system combines multiple modeling approaches into a composite solution. It integrates bootstrapped machine learning models with deep learning systems, creating a hybrid architecture that leverages the interpretability of traditional models while incorporating the robustness of deep learning to handle anomalous values effectively.
Data Source
AI summary
A system for determining intents associated with human utterances at a voice response system is provided. The system may involve a machine learning classifier and a deep learning classifier. The machine learning classifier may receive labeled training data. The labeled training data may be sample utterances. Each sample utterance may be assigned an intent. The intent may be the label of the utterance. The intent may be included in a predefined plurality of intents. The machine learning classifier may include a feature engineering and extraction module. The feature engineering and extraction module may identify features specific to each intent. The machine learning classifier may label previously unlabeled utterances received in a production environment. The newly labeled utterances received in the production environment may be used as training data to train a deep learning classifier. After being trained, the deep learning classifier may receive and label previously unlabeled utterances.