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Multiple output relaxation machine learning model

A machine learning model and multi-output technology, applied in machine learning, computing models, neural learning methods, etc., can solve problems such as inability to deal with the interdependence of different output components

Inactive Publication Date: 2015-07-08
INSIDESALES COM
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  • Abstract
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AI Technical Summary

Problems solved by technology

However, conventional SP cannot handle the interdependencies between different output components
Additionally, traditional SP cannot handle problems with multiple correct output decisions for a given input

Method used

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  • Multiple output relaxation machine learning model
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  • Multiple output relaxation machine learning model

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Embodiment Construction

[0028]Some embodiments described herein include methods employing multiple output relaxation (MOR) machine learning models to predict multiple interdependent output components of multiple output dependent (MOD) output decisions. The example methods disclosed herein can be used to solve the MOD problem.

[0029] As used herein, the term "multiple output dependency" or "MOD" refers to an output decision, or a problem with output decisions, that includes multiple output components that are interdependent in that each component depends not only on depends on the input and depends on other components. Some example MOD questions include, but are not limited to: 1) a mix of stocks to buy to balance a mutual fund given current stock market conditions; 2) a mix of players to add to a sports team's roster given an opposing team's current roster; And 3) The combination of shirt, pants, belt and shoes to wear given the current weather conditions. In each of these examples, each componen...

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Abstract

A multiple output relaxation (MOR) machine learning model. In one example embodiment, a method for employing an MOR machine learning model to predict multiple interdependent output components of a multiple output dependency (MOD) output decision may include training a classifier for each of multiple interdependent output components of an MOD output decision to predict the component based on an input and based on all of the other components. The method may also include initializing each possible value for each of the components to a predetermined output value. The method may further include running relaxation iterations on each of the classifiers to update the output value of each possible value for each of the components until a relaxation state reaches an equilibrium or a maximum number of relaxation iterations is reached. The method may also include retrieving an optimal component from each of the classifiers.

Description

technical field [0001] Embodiments discussed herein relate to multiple output relaxation (MOR) machine learning models. Background technique [0002] Machine learning is a form of artificial intelligence that is used to enable computers to evolve behavior based on empirical data. Machine learning can exploit training examples to capture features of interest for an unknown underlying probability distribution of these examples. Training data can be viewed as examples showing relationships between observed variables. The main focus of machine learning research is: automatic learning to recognize complex patterns and make intelligent decisions based on data. [0003] A major difficulty in machine learning lies in the fact that, given all possible inputs, the set of all possible behaviors is too large to be covered by the set of training data. Therefore, machine learning models must generalize from the training data in order to be able to produce useful outputs in new cases. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F15/18G06N20/00
CPCH04L67/10G06N3/08G06N99/005G06N3/04G06N20/00G06N3/045
Inventor 托尼·拉蒙·马丁内斯曾信川
Owner INSIDESALES COM
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