Machine Learning Algorithm Configuration via Message Handler
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Selecting an appropriate machine learning algorithm for specific tasks is challenging due to the numerous alternatives available, each with varying suitability, and existing implementations are tightly coupled to specific tasks, making it difficult to effectively execute and arrange machine learning algorithms.
Innovation Solution
A computer-implemented method and system that includes a machine learning algorithm as an executable software component, a data store, and a message handler to receive and communicate input and output data, allowing for the determination of domain parameters and generation of output based on the algorithm's results, enabling the modification and configuration of the algorithm through input messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms are tailored specifically for each task with tight coupling, then the algorithm can achieve high accuracy for that specific task, but it becomes difficult to effectively execute and arrange machine learning algorithms and requires programmer intervention
Solution Approach 1:
The patent implements a universal machine learning algorithm framework that can handle multiple different machine learning tasks through a common interface and message handling mechanism. The system allows different algorithms (supervised, unsupervised, semi-supervised) to be executed through the same infrastructure, eliminating the need for task-specific programming while maintaining high accuracy through algorithm selection based on task characteristics.
Solution Approach 2:
The patent introduces a message handler as an intermediary component that sits between the user and the machine learning algorithm execution engine. This message handler translates high-level user requests into algorithm-specific parameters and configurations, enabling users to execute complex machine learning tasks without needing to understand the underlying algorithmic details or perform manual parameter tuning.
2Reliability
If machine learning algorithms are tailored specifically for tasks, then the algorithm performance is optimized, but the system complexity increases and programmer intervention is required
Solution Approach 1:
The patent segments the machine learning system into distinct modular components: algorithm selection module, parameter determination module, message handling module, and execution module. Each component has a specific responsibility and can be independently configured or replaced. This segmentation allows the system to maintain high performance through specialized algorithms while reducing overall complexity through clear separation of concerns and standardized interfaces.
3Productivity
If traditional machine learning implementations are used, then algorithms can process data, but they lack flexibility for modification and replacement without programmer intervention
Solution Approach 1:
The patent implements a dynamic machine learning system where algorithms, parameters, and configurations can be modified and replaced at runtime through message passing. The system allows for dynamic algorithm selection based on task requirements, dynamic parameter adjustment based on data characteristics, and dynamic replacement of algorithms without requiring system shutdown or programmer intervention. This dynamic approach enables both high productivity and adaptability.
Data Source
AI summary
A computer implemented method of executing a software module includes a machine learning algorithm as an executable software component configurable to approximate a function relating a domain data set to a range data set; a data store; and a message handler as an executable software component arranged to receive input data and communicate output data for the module, wherein the message handler is adapted to determine domain parameters for the algorithm based on the input data and to generate the output data based on a result generated by the algorithm, the method including generating a message as input data for the module, the message including instructions for execution by the module to effect a modification of the machine learning algorithm of the module.