Probability Distribution Learning for Multi-Peak Model Accuracy
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Solution Overview
Problem
Existing machine learning techniques face difficulties in accurately modeling complex probability distributions, particularly multi-peaked distributions, whether with or without pre-existing learning data, due to challenges in preparing appropriate learning data and determining model parameters.
Innovation Solution
A multi-stage learning approach is employed, starting with a simpler probability distribution and progressively refining it to match the objective distribution, using a parameter that increases in complexity, ensuring high accuracy by leveraging a monotonous increasing sequence of parameter values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a complex probability distribution is modeled directly using existing machine learning techniques, then the model structure can be comprehensive, but the learning accuracy deteriorates due to difficulties in preparing appropriate learning data and determining model parameters
Solution Approach 1:
The patent segments the learning process into multiple stages, where each stage learns a probability distribution with a specific number of peaks. The learning process is divided into sequential stages (first stage, second stage, etc.), where each stage focuses on learning a distribution with progressively more peaks, breaking down the complex task of learning multi-peaked distributions into manageable sub-tasks
Solution Approach 2:
The patent performs preliminary action by first learning a simple probability distribution with fewer peaks before attempting to learn the complex objective distribution. The first stage learns a distribution with fewer peaks as a preliminary step, and the second stage uses this learned distribution as initialization to learn the target multi-peaked distribution, preparing the model in advance for the more complex learning task
2Stability of the object's composition
If a probability distribution with fewer peaks is learned first, then the learning process becomes simpler and more stable, but the model accuracy relative to the objective distribution is initially lower
Solution Approach 1:
The patent performs preliminary action by first learning a simple probability distribution with fewer peaks before attempting to learn the complex objective distribution. The first stage learns a distribution with fewer peaks as a preliminary step, and the second stage uses this learned distribution as initialization to learn the target multi-peaked distribution, preparing the model in advance for the more complex learning task
Solution Approach 2:
The patent applies dynamics by allowing the model complexity to change over time through the multi-stage process. The number of peaks in the learned distribution dynamically increases from stage to stage, starting with fewer peaks for stability and progressively increasing to match the objective distribution's complexity, adapting the model structure to the learning requirements at each stage
Data Source
AI summary
Provided is a non-transitory computer-readable medium having stored therein a learning program for causing a computer to execute a process. The process includes a first process of generating a probability distribution model by learning a probability distribution having fewer peaks than an objective probability distribution, and a second process of generating a new probability distribution model by learning a probability distribution closer to the objective probability distribution than a learned probability distribution using a parameter of a generated probability distribution model.