The high generalization ability of support vector machines (SVMs) has been shown in many practical applications, however, they are considerably slower in test phase than other learning approaches due to the possibly big number of support vectors comprised in their solution. In this letter, we describe a method to reduce such number of support vectors. The reduction process iteratively selects two nearest support vectors belonging to the same class and replaces them by a newly constructed one. Through the analysis of relation between vectors in input and feature spaces, we present the construction of the new vectors that requires to find the unique maximum point of a one-variable function on (0,1), not to minimize a function of many variables with local minima in previous reduced set methods. Experimental results on real life dataset show that the proposed method is effective in reducing number of support vectors and preserving machine's generalization performance.

In this paper we describe a new method to reduce the complexity of support vector machines by reducing the number of necessary support vectors included in their solutions. The reduction process iteratively selects two nearest support vectors belonging to the same class and replaces them by a newly constructed vector. Through the analysis of relation between vectors in the input and feature spaces, we present the construction of new vectors that requires to find the unique maximum point of a one-variable function on the interval (0, 1), not to minimize a function of many variables with local minimums in former reduced set methods. Experimental results on real life datasets show that the proposed method is effective in reducing number of support vectors and preserving machine's generalization performance.

The hepatitis temporal database collected at Chiba university hospital between 1982--2001 was recently given to challenge the KDD research. The database is large where each patient corresponds to 983 tests represented as sequences of irregular timestamp points with different lengths. This paper presents a temporal abstraction approach to mining knowledge from this hepatitis database. Exploiting hepatitis background knowledge and data analysis, we introduce new notions and methods for abstracting short-term changed and long-term changed tests. The abstracted data allow us to apply different machine learning methods for finding knowledge part of which is considered as new and interesting by medical doctors.

Viewing knowledge discovery as a user-centered process that requires an effective collaboration between the user and the discovery system, our work aims to support an active role of the user in that process by developing synergistic visualization tools integrated in our discovery system D2MS. These tools provide an ability of visualizing the entire process of knowledge discovery in order to help the user with data preprocessing, selecting mining algorithms and parameters, evaluating and comparing discovered models, and taking control of the whole discover process. Our case-studies with two medical datasets on meningitis and stomach cancer show that, with visualization tools in D2MS, the user gains better insight in each step of the knowledge discovery process as well the relationship between data and discovered knowledge.