Improving learning vector quantization using data reduction
نویسندگان
چکیده
منابع مشابه
Learning vector quantization for proximity data
Prototype-based classifiers such as learning vector quantization (LVQ) often display intuitive and flexible classification and learning rules. However, classical techniques are restricted to vectorial data only, and hence not suited for more complex data structures. Therefore, a few extensions of diverse LVQ variants to more general data which are characterized based on pairwise similarities or...
متن کاملLearning Vector Quantization for Multimodal Data
Learning vector quantization (LVQ) as proposed by Kohonen is a simple and intuitive, though very successful prototype-based clustering algorithm. Generalized relevance LVQ (GRLVQ) constitutes a modification which obeys the dynamics of a gradient descent and allows an adaptive metric utilizing relevance factors for the input dimensions. As iterative algorithms with local learning rules, LVQ and ...
متن کاملLearning vector quantization for heterogeneous structured data
In this paper we introduce an approach to integrate heterogeneous structured data into a learning vector quantization. The total distance between two heterogeneous structured samples is defined as a weighted sum of the distances in the single structural components. The weights are adapted in every iteration of learning using gradient descend on the cost function inspired by Generalized Learning...
متن کاملImproving Bankruptcy Prediction with Hidden Layer Learning Vector Quantization
A Hidden Layer Learning Vector Quantization (HLVQ), neural networklearning algorithm is used for correcting the outputs of Multilayer Perceptrons (MLP) for predicting corporate bankruptcy. We call this method HLVQ-C, and it is shown that it outperforms both discriminant analysis and traditional neural networks while significantly reducing type I error, which is the type of error that has the hi...
متن کاملSpeech Data Compression using Vector Quantization
Mostly transforms are used for speech data compressions which are lossy algorithms. Such algorithms are tolerable for speech data compression since the loss in quality is not perceived by the human ear. However the vector quantization (VQ) has a potential to give more data compression maintaining the same quality. In this paper we propose speech data compression algorithm using vector quantizat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Advances in Intelligent Informatics
سال: 2019
ISSN: 2548-3161,2442-6571
DOI: 10.26555/ijain.v5i3.330