To avoid of manipulating search engines results by web spam, anti spam system use machine learning techniques to detect spam. However, if the learning set for the system is out of date the quality of classification falls rapidly. We present the web spam recognition system that periodically refreshes the learning set to create an adequate classifier. A new classifier is trained exclusively on data collected during the last period. We have proved that such strategy is better than an incrementation of the learning set. The system solves the starting–up issues of lacks in learning set by minimisation of learning examples and utilization of external data sets. The system was tested on real data from the spam traps and common known web services: Quora, Reddit, and Stack Overflow. The test performed among ten months shows stability of the system and improvement of the results up to 60 percent at the end of the examined period.
The paper deals with the new method of automatic vehicle classification called ALT (ALTernative). Its characteristic feature is versatility resulting from its open structure, moreover a user can adjust the number of vehicles and their category according to individual requirements. It uses an algorithm for automatic vehicle recognition employing data fusion methods and fuzzy sets. High effectiveness of classification while retaining high selectivity of division was proved by test results. The effectiveness of classification of all vehicles at the level of 95% and goods trucks of 100% is more than satisfactory.