One Class SVM 是指你的training data 只有一类positive (或者negative)的data, 而没有另外的一类.在这时,你需要learn的实际上你training data 的boundary.而这时不能使用 maximum margin 了,因为你没有两类的data. 所以呢,在这边文章中,“Estimating the support of a high-dimensional distribution”, Schölkopf 假设最好的boundary要远
可以直接拷贝运行. package year; import java.util.Scanner; public class year { public static void main(String[] args) { // TODO Auto-generated method stub year y = new year(); y.fun(); } public void fun(){ String str = shuru(); int year = Integer.parseInt(str
public class Test { public static void main(String[] args) { System.out.println(recursive(30)); } public static int recursive(int i){ // int a0=1; // int a1=1; // int a2=a1+a0; // int a3=a2+a1; if (i==0||i==1) return 1; return recursive(--i)+recursiv
// test20.cpp : 定义控制台应用程序的入口点. // include "stdafx.h" include include include include include include include include using namespace std; class Solution { public: int NumberOf1Between1AndN_Solution(int n) { // int num=1; int count = 0; for (int
个数: var str = 'aaafsdsaaasasasasaa'; var json = {}; for (var i = 0; i < str.length; i++) { if(!json[str.charAt(i)]){ json[str.charAt(i)] = 1; }else{ json[str.charAt(i)]++; } }; console.log(json); var iMax = 0; var iIndex = ''; for(var key in json){ a