Universal machine learning building block
AssigneeKnowmTech, LLC
InventorsAlex NUGENT, Timothy MOLTER
Life of this patent
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Abstract
A universal machine learning building block, comprising in some embodiments a differential pair of output electrodes, wherein each electrode comprises a plurality of input lines coupled to it via collections of meta-stable switches. In other embodiments, a methodology can be implemented in the context of hardware and/or software for deriving linear neurons implementing an AHaH plasticity rule and generating an AHaH node(s) that can include one or more such linear neurons, wherein the AHaH node(s) functions according to an AHaH rule. Some embodiments can also include an AHaH classifier and/or AHaH cluster that include one or more such AHaH nodes.
Claims19
A universal machine learning building block apparatus, said apparatus comprising:
- at least one meta-stable switch;
- and
- a differential pair of output electrodes, wherein each electrode among said differential pair of output electrodes comprises a plurality of input lines coupled thereto via said at least one meta-stable switch.
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A machine learning method, comprising:
- deriving a plurality of linear neurons implementing an AHaH (Anti-Hebbian and Hebbian) plasticity rule;
- and
- generating at least one AHaH node that comprises said plurality of linear neurons, wherein said at least one AHaH node functions according to an AHaH rule to maximize a margin between positive classes and negative classes.
A machine learning system, comprising:
- a computer-usable medium embodying computer program code comprising instructions executable and configured for:
- deriving a plurality of linear neurons implementing an AHaH (Anti-Hebbian and Hebbian) plasticity rule;
- and
- generating at least one AHaH node that comprises said plurality of linear neurons, wherein said at least one AHaH node functions according to an AHaH rule to maximize a margin between positive classes and negative classes.
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Dependencies are inferred from the claim text.