- Machine Learning with Swift
- Alexander Sosnovshchenko
- 65字
- 2021-06-24 18:55:07
Utilizing state transitions
Transitions between some motion types are more likely than between others: it's easy to imagine how a user can start walking after being still, but it's much harder to imagine how he could start running immediately after squatting. The popular way of modelling such probabilistic state changes is hidden Markov model (HMM), but that's a long story for some other time.
推薦閱讀
- 深入理解Spring Cloud與實戰
- 數字道路技術架構與建設指南
- Effective STL中文版:50條有效使用STL的經驗(雙色)
- 基于ARM的嵌入式系統和物聯網開發
- 微服務分布式架構基礎與實戰:基于Spring Boot + Spring Cloud
- Apple Motion 5 Cookbook
- 基于Apache Kylin構建大數據分析平臺
- 電腦高級維修及故障排除實戰
- Machine Learning with Go Quick Start Guide
- SiFive 經典RISC-V FE310微控制器原理與實踐
- VMware Workstation:No Experience Necessary
- 數字媒體專業英語(第2版)
- Managing Data and Media in Microsoft Silverlight 4:A mashup of chapters from Packt's bestselling Silverlight books
- Spring Cloud微服務和分布式系統實踐
- Hands-On Motion Graphics with Adobe After Effects CC