算法思维举要

最新推荐文章于 2024-10-20 22:30:27 发布

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本文探讨了问题解决的优化策略，分析了连续与离散解决方案的区别，讨论了暴力搜索、分支限界法及不确定环境下的问题解决。深入解读了机器学习五大流派：符号主义、连接主义、进化论、贝叶斯学派和类比主义者的特点与应用，为理解人工智能提供了多元视角。

Everything is optimization. Problem solving = Representation->evaluation->optimization.
The solution is continuous or discrete ?
If discrete, could brute-force or branching be a choice ?
The environment has uncertainty ?
The problem can be divided and conquer ?
The problem can be solved recursively ?
Can we trade time(space) with space(time) ?
About artificial intelligence : 机器学习五大流派（https://cloud.tencent.com/developer/article/1053989）
1. My Understanding:
  1. Symbolists : won't work in real life
  2. Connectionists: like inverse deduction by symbolists, but with BP
  3. Evolutionaries: heuristics. May be promising for problems with a structure, e.x. combinatorial problems
  4. Bayesians: master of probabilities. Useful in any real-life circumstances.
  5. Anologizers: never forget similarity. Well employed in unsupervised learning.