System and method to enable training a machine learning network in the presence of weak or absent training exemplars
First Claim
1. A method, comprising:
- initializing at least one of i)nodes in a first machine learning network and ii) connections between the nodes, to a predetermined strength value, wherein the nodes represent factors determining an output of the network;
providing a first set of questions to a plurality of users, the first set of questions relating to at least one of the factors;
receiving guesstimates from the users in response to the first set of questions;
adjusting the predetermined strength value as a function of the guesstimates; and
combining guesstimates received from the users with those of other users to develop and evaluate the network, which is a consensus network.
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Accused Products
Abstract
Described is a system and method for training a machine learning network. The method comprises initializing at least one of nodes in a machine learning network and connections between the nodes to a predetermined strength value, wherein the nodes represent factors determining an output of the network, providing a first set of questions to a plurality of users, the first set of questions relating to at least one of the factors, receiving at least one of choices and guesstimates from the users in response to the first set of questions and adjusting the predetermined strength value as a function of the choices/guesstimates. The real and simulated examples presented demonstrate that synthetic training sets derived from expert or non-expert human guesstimates can replace or augment training data sets comprised of actual training exemplars that are too limited in size, scope, or quality to otherwise generate accurate predictions.
30 Citations
57 Claims
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1. A method, comprising:
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initializing at least one of i)nodes in a first machine learning network and ii) connections between the nodes, to a predetermined strength value, wherein the nodes represent factors determining an output of the network; providing a first set of questions to a plurality of users, the first set of questions relating to at least one of the factors; receiving guesstimates from the users in response to the first set of questions; adjusting the predetermined strength value as a function of the guesstimates; and combining guesstimates received from the users with those of other users to develop and evaluate the network, which is a consensus network. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27)
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28. A device, comprising:
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a communications arrangement receiving guesstimates from a plurality of users in response to questions related to a predetermined topic; and a processor initializing at least one of i) nodes in a machine learning network and ii) connections between the nodes, to a predetermined strength value, the processor adjusting the predetermined strength value as a function of the choices, wherein the initialization is performed prior to any input of actual input and actual output into the network; wherein the network is a consensus network and the users are able to combine their own guesstimates with those of other users to develop and evaluate the network. - View Dependent Claims (29, 30, 31, 32, 33, 34, 35, 36)
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37. A system, comprising:
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a host computing device including a machine learning network, the host computing device initializing at least one of i) nodes in the network and ii) connections between the nodes, to a respective predetermined strength value, the host computing device outputting questions, each question corresponding to at least one of the connections; and a plurality of client computing devices receiving the questions, the client computing devices transmitting guesstimates from users thereof in response to the questions, wherein the host computing device adjusts the predetermined strength value as a function of the guesstimates, and wherein the network is a consensus network and the users are able to combine their own guesstimates with those of other users to develop and evaluate the network. - View Dependent Claims (38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49)
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50. A non-transitory computer-readable medium storing a set of instructions for execution by a processor to perform a method comprising:
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initializing at least one of i) nodes in a machine learning network and ii) connections between the nodes, to a predetermined strength value, wherein the nodes represent factors determinative of an output of the network; providing at least one question to a plurality of users, the at least one question relating to at least one of the factors; receiving guesstimates from the users in response to the at least one question; adjusting the predetermined strength value as a function of the choices; and combining guesstimates received from the users with those of other users to develop and evaluate the network, which is a consensus network. - View Dependent Claims (51, 52, 53, 54, 55, 56, 57)
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Specification