Ancient white text recognition algorithm based on partial label learning

[0042] The ancient Baiwen recognition algorithm based on partial label learning includes the following steps:

[0043] Step 1: first input the recognition algorithm formula inside the recognition device: input D={(X i , S i )丨1≤i≤m}: Partially labeled data training set, enter the recognition formula again: D * ={x i 丨1≤i≤m *}, this is the partially labeled data test set;

[0044] Step 2: Use two formulas to carry out forward label propagation weights, and then carry out reverse label propagation weights;

[0045] Step 3: Use the formula to correct the initial confidence weight, set k as the number of nearest neighbor samples, and T as the number of label propagation iterations;

[0046] Step 4: Output, y i For the training set example x i Disambiguation results, where 1≤i≤m, output y i * For the test set example x i * The classification results of , where 1≤i≤m * .

[0047] Step 5: Obtain the k-nearest neighbor relationship of each example, and solve the connection w

[0059] The ancient Baiwen recognition algorithm based on partial label learning includes the following steps:

[0060] Step 1: first input the recognition algorithm formula inside the recognition device: input D={(X i , S i )丨1≤i≤m}: Partially labeled data training set, enter the recognition formula again: D * ={x i 丨1≤i≤m *}, this is the partially labeled data test set;

[0061] Step 2: Use two formulas to carry out forward label propagation weights, and then carry out reverse label propagation weights;

[0062] Step 3: Use the formula to correct the initial confidence weight, set k as the number of nearest neighbor samples, and T as the number of label propagation iterations;

[0063] Step 4: Output, y i For the training set example x i Disambiguation results, where 1≤i≤m, output y i * For the test set example x i * The classification results of , where 1≤i≤m * .

[0064] Step 5: Obtain the k-nearest neighbor relationship of each example, and solve the connection w

[0076] The ancient Baiwen recognition algorithm based on partial label learning includes the following steps:

[0077] Step 1: first input the recognition algorithm formula inside the recognition device: input D={(X i , S i )丨1≤i≤m}: Partially labeled data training set, enter the recognition formula again: D * ={x i 丨1≤i≤m *}, this is the partially labeled data test set;

[0078] Step 2: Use two formulas to carry out forward label propagation weights, and then carry out reverse label propagation weights;

[0079] Step 3: Use the formula to correct the initial confidence weight, set k as the number of nearest neighbor samples, and T as the number of label propagation iterations;

[0080] Step 4: Output, y i For the training set example x i Disambiguation results, where 1≤i≤m, output y i * For the test set example x i * The classification results of , where 1≤i≤m * .

[0081] Step 5: Obtain the k-nearest neighbor relationship of each example, and solve the connection w