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给定a，b，扩展欧几里得算法求得最大公约数的同时，还会给出ax+by=gcd(a,b)的整数解x，y 假设 di−2=di−1ci+di di−1=dici+1+di+1 假设a，b的最大公约数为g，当某一步的di−1=0时，$1d 阅读全文
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Independent Components Analysis Ambiguity ICA is ambiguous to scaling and permutation. but usually it doesn't matter. As long as the data is not Gauss 阅读全文
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Principal Components Analysis Intuition PCA tries to identify the subspace in which the data approximately lies. Intuitively, we choose a direction fo 阅读全文
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Decision Tree Greedy, Top-down, Recurrent Classification Tree misclassification loss is not suitable for decision tree loss, because $$ L(R_p) - (\lam 阅读全文
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Factor Analysis Background when m (number of samples) < n (dimension of samples), the covariance matrix might be singular. So it is necessary to find 阅读全文
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EM Algorithm Jensen's inequality convex function: f″ or H \ge 0 (Hessian matrix when x is a vector) E[f(x)] \ge f(EX) EM Algorithm 阅读全文
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Reinforcement Learning Background Credit Assignment Problem: Explore how actions in an action sequence contribute to the outcome finally. MDP(Markov D 阅读全文
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Learning Mathematical Theorem Feature Selection If we have n features, the VC dimension of hypothesis class would be O(n), if n>>m, over-fitting would 阅读全文
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Support Vector Machine Two Margins Functional Margin \gamma and Geometrical Margin \hat{\gamma} $$ \hat{\gamma^{(i)}}=\frac{\gamma^{(i)}}{||w||} $ 阅读全文
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Error Theorem in ML Basic Concepts \varepsilon(h) = \text{Generalization Error} = E_{(x,y)\sim D}[1(h(x) \neq y)] $$ \hat{\varepsilon}(h)=\text{ 阅读全文
