21334257
Feature Extraction Based on the Independent Component Analysis for Text Classification
The independent component analysis (ICA) is a very popular algorithm used in the blind source separation and it has been widely used in many other fields. In this paper, the ICA is applied to text classification. We try to combine the traditional feature selection methods with ICA technology to improve the text classification performance by extracting Independent features. Further, a series of comparison experiments have been performed. The experiment results have shown that the ICA technology can indeed help to improve the classification performance and the combined method has showed the clear advantages.
34135450
A New Text Feature Conversion Method for Text Classification
Keywords normally carry large amount of category information. In order to fully utilize this kind of information for text classification, this paper proposes a new text feature conversion method based on the SKG model. The method uses the classified texts with the listed key words as the training data to train the classifier. To expand the keyword space, we construct the KWB model and do the text classification by combining the KWB model and the SKG model. The experiment results demonstrate the advantages of this new method.
20762776
A Symmetric Orthogonal FastICA Algorithm and Applications in EEG
Extracting a number of independent components,FastICA algorithm can ensure that, each component extracted has never been extracted by adding orthogonalization steps. However, Gram-Schmidt orthogonal method implemented itself that estimation error of the first vector has been accumulated in the subsequent vectors. In this paper, the square root of the classical matrix method is used to achieve symmetric orthogonal that parallel to estimate the source of variables and avoid the Gram-Schmidt orthogonal method of estimation error accumulation problem. The symmetric orthogonal FastICA algorithm is apply in removal EEG artifacts (including eye movement, blinking, ECG, EMG, etc). The experiment and simulation results demonstrate that the symmetry orthogonal FastICA algorithm removes the artifacts and has a better separation efficiency and fast convergence.
12523556
Design Patterns of Scalable Cluster System Software
The design pattern of cluster system software has an important influence on scalability of massive cluster system. The paper presents design patterns of scalable cluster system software, including scalable software topologies and optimized communication modes. These design patterns have been widely applied in Dawning series of supercomputers and some results of performance evaluation show their good scalability
29743973
SVM Model Based on Signal Transformation and its Applications in Oil Water-Flooded Identification
this paper, a method of signal transformation for feature extraction is proposed. It can transform log-signal space into the vector space, which the experiment system requires, and then use SVM (Support Vector Machine) automatically to identify the water-flooded status of oil-saturated stratum. The results of experiment indicate that this algorithm has good identification ability and strong generalization ability in condition that the number of training swatch is limited.
9462236
PSDBoost: Matrix-Generation Linear Programming for Positive Semidefinite Matrices Learning
In this work, we consider the problem of learning a positive semidefinite matrix. The critical issue is how to preserve positive semidefiniteness during the course of learning. Our algorithm is mainly inspired by LPBoost [1] and the general greedy convex optimization framework of Zhang [2]. We demonstrate the essence of the algorithm, termed PSDBoost (positive semidefinite Boosting), by focusing on a few different applications in machine learning. The proposed PSDBoost algorithm extends traditional Boosting algorithms in that its parameter is a positive semidefinite matrix with trace being one instead of a classifier. PSDBoost is based on the observation that any trace-one positive semidefinite matrix can be decomposed into linear convex combinations of trace-one rank-one matrices, which serve as base learners of PSDBoost. Numerical experiments are presented.
31093950
Extended Password Recovery Attacks against APOP, SIP, and Digest Authentication
In this paper, we propose password recovery attacks against challenge-response authentication protocols. Our attacks use a message difference for a MD5 collision attack proposed in IEICE 2008. First, we show how to efficiently find a message pair that collides with the above message difference. Second, we show that a password used in authenticated post office protocol (APOP) can be recovered practically. We also show that the password recovery attack can be applied to a session initiation protocol (SIP) and digest authentication. Our attack can recover up to the first 31 password characters in a short time and up to the first 60 characters faster than the naive search method. We have implemented our attack and confirmed that 31 characters can be successfully recovered.