Recognition Model Training Platform Shared Data Sets
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Solution Overview
Problem
Researchers face challenges in creating and evaluating pattern recognition models due to the lack of shared data sets and computationally intensive processes, which limit the accuracy and efficiency of recognition models across different organizations.
Innovation Solution
A web-based open research platform with modules for data collection, training, evaluation, and plugin capabilities allows users to create and evaluate recognition models using backend CPU resources, enabling efficient training and evaluation of recognition models across a shared environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If researchers collect and train recognition models using their own private data samples independently, then each researcher can maintain data security and control, but the overall recognition accuracy is limited due to insufficient data diversity and quantity
Solution Approach 1:
The patent merges multiple independent data sets from different researchers into a unified shared data set. The system allows researchers to contribute their data samples to a common repository that can be accessed by all participants, thereby combining the strengths of multiple data sources to improve overall recognition accuracy while maintaining individual data ownership through controlled access mechanisms.
Solution Approach 2:
The patent creates a universal platform that serves multiple functions: data collection, model training, evaluation, and sharing. This multi-functional system allows the same infrastructure to support various recognition tasks and algorithms, enabling researchers to benefit from a common resource that adapts to different research needs and objectives.
2Adaptability or versatility
If researchers use their own private data samples and algorithms for model training, then each researcher maintains independence and control, but comparing recognition models against one another becomes infeasible
Solution Approach 1:
The patent segments the research process into distinct modular components: data collection module, model training module, evaluation module, and comparison module. Each module operates independently but interfaces with standardized protocols, allowing researchers to contribute their algorithms and data while the system handles the integration and comparison automatically, reducing the complexity of direct researcher-to-researcher integration.
Solution Approach 2:
The patent introduces a central platform as an intermediary between researchers' independent systems. This mediator handles the complex tasks of data standardization, model training coordination, and performance comparison, allowing researchers to maintain independence in their local systems while achieving unified comparison through the intermediary platform.
3Measurement precision
If recognition algorithms are trained on large data sets to achieve high accuracy, then recognition precision improves, but the computational time increases to several weeks on a single machine
Solution Approach 1:
The patent combines multiple computing resources into a unified training infrastructure. By pooling computational power from multiple machines into a shared computing environment, the system can process large data sets in parallel, significantly reducing training time while maintaining the ability to achieve high recognition precision through comprehensive data analysis.
Solution Approach 2:
The patent implements preliminary data processing and feature extraction steps that prepare data in advance for efficient training. By pre-processing data sets and extracting relevant features before the main training process, the system reduces the computational burden during actual model training, thereby decreasing training time without compromising recognition precision.
4Reliability
If recognition models are trained on large data sets to achieve high accuracy, then model performance improves, but the computational cost becomes very expensive and time consuming
Solution Approach 1:
The patent segments the training process into multiple stages with progressively increasing complexity. Early stages use simplified models and smaller data subsets to establish baseline performance, while later stages refine the models using full data sets. This segmented approach achieves high final accuracy while distributing computational costs across multiple manageable phases rather than requiring intensive single-phase processing.
Solution Approach 2:
The patent implements progressive training where models are first trained on partial data sets to achieve reasonable baseline accuracy, then progressively exposed to larger portions of the full data set. This partial action approach allows the system to achieve acceptable model accuracy with reduced computational cost, while still benefiting from the option to improve further with additional training resources.
Data Source
AI summary
A method for researching and developing a recognition model in a computing environment, including gathering one or more data samples from one or more users in the computing environment into a training data set used for creating the recognition model, receiving one or more training parameters defining a feature extraction algorithm configured to analyze one or more features of the training data set, a classifier algorithm configured to associate the features to a template set, a selection of a subset of the training data set, a type of the data samples, or combinations thereof, creating the recognition model based on the training parameters, and evaluating the recognition model.