Verified65% confidenceFactExact time
The Sequential Minimal Optimization algorithm (SMO) was proposed by John Platt in 1998, decomposing large-scale quadratic programming problems into minimal sub-problems involving only two variables.
3
Sources
65%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
Do You Still Need to Hand-Code Machine Learning Algorithms in the AI Era? The Answer and the Method
redditr/learnmachinelearning7/11/2026
Related Claims
UnverifiedThe core idea of SMO is to decompose a large optimization problem into a series of minimal sub-problems, selecting only two parameters for optimization at a time and deriving an analytical solution, thereby avoiding time-consuming numerical solving.78% similarUnverifiedPrim最小生成树算法（1957年）和Dijkstra最短路径算法（1959年）是贪心思想的早期应用64% similarUnverifiedbase-stock 策略在需求服从独立同分布、提前期固定的假设下可被严格证明是最小化总成本的解析最优解61% similarUnverifiedStrategies such as Monte Carlo Tree Search that rely on executed rewards to rank nodes can only make decisions based on very few and noisy scores under a limited compute budget.60% similarVerifiedTop-P Nucleus Sampling was proposed by Holtzman et al. in 2020, sampling from the minimum vocabulary subset whose cumulative probability reaches threshold P through dynamic truncation.60% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/498455API
curl https://kongchang.com/api/v1/knowledge/claims/498455MCP
get_claim(id=498455)