What is the difference in SMO algorithm for SVM and SMO for one class?
Both the papers attempt to develop Sequential Minimization Optimization (SMO) approaches for two kinds of Support Vector Machine (SVM) formulations, which include the conventional SVM and one-class SVM. The basic idea behind the SMO approach is to make the underlying SVM formulation scalable for larger datasets. This is a challenge since the SVM is a kernel method, and using it for large datasets requires computation of the kernel matrix whose size is of the order of the number of training samples. Thus, there is a high memory and computational requirement, moreover, issues pertaining to matrix stability may arise. In order to address this issue, the SMO approach adopts a method where pair-wise Lagrange multipliers are optimized instead of solving the entire Quadratic Programming Problem (QPP) as in the conventional case. The selection of the Lagrange multipliers may be based on heuristics. This (SMO) makes the approach (SVM) scalable for larger datasets, which is the underlying similarity between the use of SMO for SVM and one-class SVM.

In order to understand the difference between the two, it is important to understand the difference between the purpose of the SVM and one-class SVM, and subsequently the difference in their formulations (the optimization problem for each). This eventually translates into the difference in applying the SMO strategy for each of these cases. 

The conventional SVM for binary classification aims to find a separating hyperplane $w^Tx+b=0$ with coefficients $w$ and bias $b$ for training data $x$ (and class labels $y$) with maximum margin ($\frac{1}{\|w\|_2}$) which is at least unit distance from points of the two classes which lie close to the separating hyperplane. This is achieved by the soft-margin formulation involving trade-off using positively constrained slack variables $\xi$ and user-defined parameter $C$ as below:

$\min_{w,b,\xi} \frac{1}{2}  \|w\|_2^2 + C \sum_i \xi_i$

subject to

$y^i (w^T x^i + b) + \xi_i\geq 1$,
$\xi_i \geq 0$

In  practice, the kernel trick is used to map the data to a high-dimensional kernel space $K$ that induces linear separability and the dual of the above formulation in terms of the Lagrange multipliers $\alpha$ is solved, which is given as

$\min_{\alpha} \frac{1}{2} \sum_i \sum_j y^i y^j K(x^i,x^j) \alpha_i \alpha_j - \sum_i \alpha_i$ 

subject to

$\sum_i \alpha_i y^i = 0, \alpha_i \geq 0$

On the other hand, in case of the one-class SVM, we would like to determine whether the test sample belongs to a specific class, determined by the training data (having $M$ samples), or not. Consequently, the one-class SVM is solved by

$\min_{w,b,\xi} \frac{1}{2} \|w\|_2^2 + \frac{1}{C M} \sum_i \xi_i - b$

subject to

$w^T x^i \geq (b - \xi_i)$,
$\xi_i \geq 0$

The dual for the one-class SVM is then given as

$\min_{\alpha} \frac{1}{2} \sum_i \sum_j  K(x^i,x^j) \alpha_i \alpha_j$ 

subject to

$0 \leq \alpha_i \leq \frac{1}{CM}, \sum_i \alpha_i = 1$

The derivation of the SMO algorithm follows the steps presented in the two papers, and the difference arises out of the nature of the two SVM formulations and their corresponding dual problems. Details of the derivation may be found in the respective papers, and newer strategies for selection of the Lagrange multipliers for optimization may also be found in literature.