Micron technology, inc. (20240185139). SPACE EFFICIENT RANDOM DECISION FOREST MODELS IMPLEMENTATION UTILIZING AUTOMATA PROCESSORS simplified abstract
Contents
- 1 SPACE EFFICIENT RANDOM DECISION FOREST MODELS IMPLEMENTATION UTILIZING AUTOMATA PROCESSORS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SPACE EFFICIENT RANDOM DECISION FOREST MODELS IMPLEMENTATION UTILIZING AUTOMATA PROCESSORS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about the Technology
- 1.13 Original Abstract Submitted
SPACE EFFICIENT RANDOM DECISION FOREST MODELS IMPLEMENTATION UTILIZING AUTOMATA PROCESSORS
Organization Name
Inventor(s)
Yao Fu of Castro Valley CA (US)
Paul Glendenning of Woodside CA (US)
Tommy Tracy, Ii of Charlottesville VA (US)
Eric Jonas of San Francisco CA (US)
SPACE EFFICIENT RANDOM DECISION FOREST MODELS IMPLEMENTATION UTILIZING AUTOMATA PROCESSORS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185139 titled 'SPACE EFFICIENT RANDOM DECISION FOREST MODELS IMPLEMENTATION UTILIZING AUTOMATA PROCESSORS
Simplified Explanation
An apparatus processes a feature vector of a data stream to generate a final classification by calculating feature labels, providing them to another processing resource, receiving classifications for each label, and combining them.
- The apparatus receives a feature vector with feature values.
- It calculates feature labels based on the feature values.
- The labels are provided to another processing resource.
- Classifications are received for each label based on the range of feature values.
- The apparatus combines the classifications to generate a final classification of the data stream.
Key Features and Innovation
- Processing resource calculates feature labels from feature values.
- Labels are provided to another processing resource for classification.
- Classifications are generated based on the range of feature values.
- Final classification of the data stream is produced by combining the classifications.
Potential Applications
This technology can be applied in various fields such as:
- Data analysis
- Pattern recognition
- Machine learning
Problems Solved
- Efficient classification of data streams
- Automation of feature labeling and classification
- Improved accuracy in data analysis
Benefits
- Streamlined data processing
- Enhanced classification accuracy
- Automation of repetitive tasks
Commercial Applications
- Data analytics software
- Machine learning platforms
- Automation systems
Prior Art
No prior art information available at this time.
Frequently Updated Research
No frequently updated research available at this time.
Questions about the Technology
Question 1
How does the apparatus handle feature values with a wide range of variability?
The apparatus is designed to calculate feature labels and generate classifications based on the respective range of feature values, allowing for flexibility in handling varying data ranges.
Question 2
Can the apparatus be integrated into existing data processing systems seamlessly?
Yes, the apparatus can be integrated into existing systems as it is configured to receive feature vectors and provide final classifications, making it compatible with different data processing setups.
Original Abstract Submitted
an apparatus includes a processing resource configured to receive a feature vector of a data stream. the feature vector includes a set of feature values. the processing resource is further configured to calculate a set of feature labels based at least in part on the set of feature values to generate a label vector, provide the label vector to another processing resource, and to receive a plurality of classifications corresponding to each feature label of the label vector from the other processing resource. the plurality of classifications are generated based at least in part on a respective range of feature values of the set of feature values. the processing resource is configured to then combine the plurality of classifications to generate a final classification of the data stream.
