文献Classification_of_remote_sensed_images_using_random_forests_and_deep_learning_framework译文

模式识别第一章作业题，中科院刘成林，Question 1 (Pattern Classification, Chapter 2, Problem 12) Let ωmax(x) be the state of nature for which P(ωmax|x) ≥ P(ωi|x) for all i, i = 1,...,c. (a) Show that P(ωmax|x) ≥ 1/c (b) Show that for the minimum-error-rate decision rule the average probability of error is given by P(error) = 1−RP(ωmax|x)p(x)dx (c) Use these two results to show that P(error) ≤ (c−1)/c (d) Describe a situation for which P(error) = (c−1)/c Question 2 (Pattern Classification, Chapter 2, Problem 13) In many pattern classification problems one has the option either to assign the pattern to one of c classes, or to reject it as being unrecognizable. If the cost for rejects is not too high, rejection may be a desirable action. Let λ(αi|ωi) =     0 i = j i,j = 1,...,c λr i = c + 1 λs otherwise where λr is the loss incurred for choosing the (c + 1)th action, rejection, and λs is the loss incurred for making a substitution error. Show that the minimum risk is obtained if we decide ωi if P(ωi|x) ≥ P(ωi|x) for all j and if P(ωi|x) ≥ 1− λr λs , and reject otherwise. What happens if λr = 0? What happens if λr > λs? Question 3 Now we have N samples, and each sample xi, i = 1,...,N has d-dimensions. Please provide us the proofs and the pseudo-codes of PCA algorithm Question 4 (Pattern Classification, Chapter 2, Problem 10) Consider the following decision rule for a two-category one-dimensional problem: Decide ω1 if x > θ; otherwise decide ω2. (a)Showtheprobabilityoferrorforthisruleisgivenby P(error) = P(ω1)Rθ−∞p(x|ω1)dx+P(ω2)R∞ θ p(x|ω2)dx (b) By differentiating, show that a necessary condition to minimize P(error) is that θ satisfy p(θ|ω1)P(ω1) = p(θ|ω2)P(ω2) (c) Does this equation define θ uniquely? (d) Give an example where a value of θ satisfying the equation actually maximizes the probability of error. Question 5 (Pattern Classification, Chapter 2, Problem 24) Consider the multivariate normal density for which σij = 0 and σii = σ2 i , i.e., Σ = diag(σ2 1,σ2 2,...,σ2 d). (a) Show that

tensorflow python cpu window 自己输入样本训练神经网络，测试，实现猫和狗两类动物的分类！！可用于学习！！样本资源少以及网络简单存在过拟合问题.

freeman目标分解结果，可以作为极化SAR图像分类特征，使用的极化SAR数据为Flevoland部分数据

简单的邮件分类，使用bayes分类器，100行正常邮件和100行垃圾邮件。