Jicheng Wang

Bio: Jicheng Wang is an academic researcher from Nanjing University. The author has contributed to research in topics: Support vector machine & Structured support vector machine. The author has an hindex of 1, co-authored 1 publications receiving 78 citations.

An approach to incremental SVM learning algorithm

Abstract: The classification algorithm that is based on a support vector machine (SVM) is now attracting more attention, due to its perfect theoretical properties and good empirical results. In this paper, we first analyze the properties of the support vector (SV) set thoroughly, then introduce a new learning method, which extends the SVM classification algorithm to the incremental learning area. The theoretical basis of this algorithm is the classification equivalence of the SV set and the training set. In this algorithm, knowledge is accumulated in the process of incremental learning. In addition, unimportant samples are discarded optimally by a least-recently used (LRU) scheme. Theoretical analyses and experimental results showed that this algorithm could not only speed up the training process, but it could also reduce the storage costs, while the classification precision is also guaranteed.

Comparison of SVM and LS-SVM for Regression

Abstract: Support vector machines (SVM) has been widely used in classification and nonlinear function estimation. However, the major drawback of SVM is its higher computational burden for the constrained optimization programming. This disadvantage has been overcome by least squares support vector machines (LS-SVM), which solves linear equations instead of a quadratic programming problem. This paper compares LS-SVM with SVM for regression. According to the parallel test results, conclusions can be made that LS-SVM is preferred especially for large scale problem, because its solution procedure is high efficiency and after pruning both sparseness and performance of LS-SVM are comparable with those of SVM

Creating Evolving User Behavior Profiles Automatically

Abstract: Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, a new approach for creating and recognizing automatically the behavior profile of a computer user is presented. In this case, a computer user behavior is represented as the sequence of the commands she/he types during her/his work. This sequence is transformed into a distribution of relevant subsequences of commands in order to find out a profile that defines its behavior. Also, because a user profile is not necessarily fixed but rather it evolves/changes, we propose an evolving method to keep up to date the created profiles using an Evolving Systems approach. In this paper, we combine the evolving classifier with a trie-based user profiling to obtain a powerful self-learning online scheme. We also develop further the recursive formula of the potential of a data point to become a cluster center using cosine distance, which is provided in the Appendix. The novel approach proposed in this paper can be applicable to any problem of dynamic/evolving user behavior modeling where it can be represented as a sequence of actions or events. It has been evaluated on several real data streams.

Methods and systems for identifying and localizing objects based on features of the objects that are mapped to a vector

Abstract: A method of identifying and localizing objects belonging to one of three or more classes, includes deriving vectors, each being mapped to one of the objects, where each of the vectors is an element of an N-dimensional space. The method includes training an ensemble of binary classifiers with a CISS technique, using an ECOC technique. For each object corresponding to a class, the method includes calculating a probability that the associated vector belongs to a particular class, using an ECOC probability estimation technique. In another embodiment, increased detection accuracy is achieved by using images obtained with different contrast methods. A nonlinear dimensional reduction technique, Kernel PCA, was employed to extract features from the multi-contrast composite image. The Kernel PCA preprocessing shows improvements over traditional linear PCA preprocessing possibly due to its ability to capture high-order, nonlinear correlations in the high dimensional image space.

Comparative study between incremental and ensemble learning on data streams: Case study

Abstract: With unlimited growth of real-world data size and increasing requirement of real-time processing, immediate processing of big stream data has become an urgent problem. In stream data, hidden patterns commonly evolve over time (i.e.,concept drift), where many dynamic learning strategies have been proposed, such as the incremental learning and ensemble learning. To the best of our knowledge, there is no work systematically compare these two methods. In this paper we conduct comparative study between theses two learning methods. We first introduce the concept of “concept drift”, and propose how to quantitatively measure it. Then, we recall the history of incremental learning and ensemble learning, introducing milestones of their developments. In experiments, we comprehensively compare and analyze their performances w.r.t. accuracy and time efficiency, under various concept drift scenarios. We conclude with several future possible research problems.