Computerized cross-language plagiarism detection has recently become essential. With the scarcity of scientific publications in Bahasa Indonesia, many Indonesian authors frequently consult publications in English in order to boost the quantity of scientific publications in Bahasa Indonesia (which is currently rising). Due to the syntax disparity between Bahasa Indonesia and English, most of the...

This paper deals with accuracy improvement of handwritten character recognition by the GLVQ (generalized learning vector quantization). In literature , the way of combining the FDA (Fisher discriminant analysis) and the GLVQ was investigated and evaluated to be effective for handwritten Chinese character recognition employing the minimum Euclidian distance classifier. In this paper, the project...

Classification with rejection is well understood for classifiers which provide explicit class probabilities. The situation is more complicated for popular deterministic classifiers such as learning vector quantisation schemes: albeit reject options using simple distance-based geometric measures were proposed [4], their local scaling behaviour is unclear for complex problems. Here, we propose a ...

A novel and computationally straightforward clustering algorithm was developed for vector quantization (VQ) of speech signals for a task of unsupervised pattern discovery (PD) from speech. The algorithm works in purely incremental mode, is computationally extremely feasible, and achieves comparable classification quality with the well-known k-means algorithm in the PD task. In addition to prese...

The basic concepts of distance based classification are introduced in terms of clear-cut example systems. The classical k-NearestNeigbhor (kNN) classifier serves as the starting point of the discussion. Learning Vector Quantization (LVQ) is introduced, which represents the reference data by a few prototypes. This requires a data driven training process; examples of heuristic and cost function b...

In classification tasks it may be wise to combine observations from different sources. In this paper, to obtain classification systems with both good generalization performance and efficiency in space and time, a learning vector quantization learning method based on combinations of weak classifiers is proposed. The weak classifiers are generated using automatic elimination of redundant hidden l...

An algorithm is proposed to prune the prototype vectors (prototype selection) used in a nearest neighbor classifier so that a compact classifier can be obtained with similar or even better performance. The pruning procedure is error based; a prototype will be pruned if its deletion leads to the smallest classification error increase. Also each pruning iteration is followed by one epoch of Learn...

The present paper introduces an adaptive algorithm for competitive training of a nearest neighbor (NN) classifier when using a very small codebook. The new learning rule is based on the well-known LVQ method, and uses an alternative neighborhood concept to estimate optimal locations of the codebook vectors. Experiments over synthetic and real databases suggest the advantages of the learning tec...

The R-rule is a heuristic algorithm for distancebased neural network (DBNN) learning. Experimental results show that the R-rule can obtain the smallest or nearly smallest DBNNs. However, the computational cost of the R-rule is relatively high because the learning vector quantization (LVQ) algorithm is used iteratively during learning. To reduce the cost of the R-rule, we investigate three appro...

This paper presents a Learning Vector Quantization (LVQ)-based temporal tracking method for semi-automatic video object segmentation. A semantic video object is initialized using user assistance in a reference frame to give initial classification of video object and its background regions. The LVQ training approximates video object and background classification and use them for automatic segmen...