Multistage Classification for Pattern Recognition
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Solution Overview
Problem
Current pattern recognition systems face challenges in efficiently classifying input data with varying dimensions and types, requiring substantial computation power and memory, and struggle with flexible configuration and real-time processing across edge, cloud, or hybrid computing architectures.
Innovation Solution
A flexible pattern recognition platform that dynamically adjusts pattern recognition engines for specific applications, allowing for edge, cloud, or hybrid computing architectures, and provides probabilistic classification and multistage classification methods to handle various data types, including image, video, audio, and text, with the ability to determine classification probabilities and trace influencing training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If artificial neural networks are used for pattern recognition, then classification accuracy is improved, but computation power and memory requirements increase substantially
Solution Approach 1:
The patent segments the classification process into multiple stages, where each stage processes a subset of features and produces intermediate results. This allows the system to achieve high classification accuracy through cumulative processing while reducing the computational burden on any single stage, thereby lowering overall power requirements compared to monolithic neural networks.
Solution Approach 2:
The system dynamically adjusts the number of stages and features processed at each stage based on the complexity of the classification task and available computational resources. This dynamic configuration enables the system to optimize between accuracy and computation power, achieving high accuracy when resources permit while conserving power when resources are limited.
2Measurement precision
If artificial neural networks are used for pattern recognition, then classification accuracy is improved, but memory requirements increase substantially
Solution Approach 1:
The patent segments both the feature set and the classification process into multiple stages. Each stage maintains only the knowledge elements and features relevant to that stage, rather than storing all features and knowledge elements in memory simultaneously. This segmentation dramatically reduces memory requirements while preserving classification accuracy through sequential processing.
Solution Approach 2:
The system performs preliminary processing and filtering of features in earlier stages, discarding irrelevant features before they consume memory resources in subsequent stages. This preliminary action reduces the amount of data that needs to be stored and processed in later stages, thereby reducing overall memory requirements.
3Measurement precision
If comprehensive pattern recognition is performed on all input data, then classification accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the input feature space and classification process into multiple parallel stages, where each stage processes a specific subset of features independently. This segmentation enables parallel processing of different feature subsets, reducing overall processing time while maintaining classification accuracy through the aggregation of results from all stages.
Solution Approach 2:
The system performs partial processing in each stage, focusing computational efforts on the most discriminative features for that particular stage rather than processing all features exhaustively. This partial action approach achieves sufficient classification accuracy with reduced processing time by avoiding unnecessary computations on less relevant features.
Data Source
AI summary
Some implementations of methods, apparatus and systems are directed to classifying data associated with input vectors. In some implementations, a multistage algorithm may be used to group knowledge elements, and to perform pattern recognition operations. In some particular implementations, multiple levels of classification may be performed in order to classify input vectors with reduced computational power.