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流行向量机 (简称MVM) 是据态叠加原理 组建而成的机器学习 算法 。据相对论 ，统一数据源的数据点间距保持相对性，继而提供了定义量子指针的可能，这可以用来人工架构叠加态流形的时空构象并保证流行变换的映射 关系。这从本质上区别于非线性降维 和支持向量机 ，后者运作机制依靠维度(DIB-N: 升N维，DDB-N: 降N维)，而MVM维度不变，见[ 1] 杰弗里·辛顿 的Capsule neural network 概念有本质上的不同，后者的根本定位于维度。
流行向量机 或 MVM 需要依靠两个基本假设(1) 同源数据点间距保持相对性; (2) 存在离散体系连续性以完成 数据点间向量全集的单向重复操作，以此构成迭代器 。此时定义单次游历数据集为一次振荡
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{\displaystyle {\overset {.}{O}}}
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{\displaystyle {\vec {A}}{_{0}},{\vec {A}}{_{1}},{\vec {A}}{_{2}}}
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{\displaystyle {\vec {A}}{_{n}}}
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{\displaystyle {\vec {A}}{_{\infty }}}
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{\displaystyle :\equiv }
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{\displaystyle {\underset {n=0}{\overset {\infty }{\circleddash }}}{\vec {A}}{_{n}}}
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{\displaystyle (\lVert {\vec {A}}{_{n}}\rVert \neq 0,\forall n\in [0,*\infty ])}
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{\displaystyle Iteration}
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{\displaystyle d\delta }
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{\displaystyle Floor}
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{\displaystyle \left({\frac {N\times {\overset {.}{o}}}{\lVert concat({\underset {n=0}{\overset {\infty }{\circleddash }}}{\vec {A}}{_{n}})\rVert }}\right)}
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{\displaystyle N}
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{\displaystyle Floor}
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{\displaystyle {\frac {dM_{T}}{d\delta }}={\frac {\lVert {\vec {\lambda }}\cdot M_{T}\rVert }{d_{H}}}}
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{\displaystyle \exists d_{H}{_{total}}:={max}\lbrace {min}\lbrace d({\vec {A}}{_{0}},{\vec {A}}{_{\infty }}),d({\vec {A}}{_{\infty }},{\vec {A}}{_{0}})\rbrace \rbrace }
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{\displaystyle \exists d_{H}{_{local}}:={max}\lbrace {min}\lbrace d({\vec {A}}{_{\infty }{_{-}{_{1}}}},{\vec {A}}{_{\infty }}),d({\vec {A}}{_{\infty }},{\vec {A}}{_{\infty }{_{-}{_{1}}}})\rbrace \rbrace =\lVert {\vec {A}}{_{n}}\rVert }
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{\displaystyle \exists d_{H}{_{total}}:=\lVert {\vec {A_{0}}}-{\vec {A_{\infty }}}\rVert }
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{\displaystyle {\underset {n=0}{\overset {\infty }{\circleddash }}}{\vec {A}}{_{n}}={\underset {n=0}{\overset {\infty }{\circledcirc }}}{\vec {A}}{_{n}}}
不同的数据集所对应最有效流形向量架构存在个别差异。
例子 :
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{\displaystyle d_{H}{_{total}}:=\lVert {\vec {0}}\rVert }
引力奇点 )
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{\displaystyle d_{H}{_{total}}:=\sum _{n=0}^{\infty }\lVert {\vec {A}}{_{n}}\rVert }
数组 )
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{\displaystyle d_{H}{_{total}}:=\lVert {\vec {A_{0}}}-{\vec {A_{\infty }}}\rVert }
流形 , 及グローバーのアルゴリズム ), 因:
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{\displaystyle {\underset {n=0}{\overset {\infty }{\circleddash }}}{\vec {A}}{_{n}}\equiv {\underset {n=0}{\overset {\infty }{\circledcirc }}}{\vec {A}}{_{n}}}
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{\displaystyle \mid \varphi _{i}\rangle }
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{\displaystyle \sum _{}^{}\lVert {\vec {A}}{_{k}}\rVert \times \mid \circledast _{k}\rangle }
MVM可以单独使用或者嵌入到更大体系的机器学习 策略中生效，回归分析 ，支持向量机 ，和深度学习 等。
Category:人工智能
Category:机器学习
Category:量子计算机
![{\displaystyle (\lVert {\vec {A}}{_{n}}\rVert \neq 0,\forall n\in [0,*\infty ])}](/media/api/rest_v1/media/math/render/svg/56769e1d35263dd264ce73dc70552a6739baa78d)
