We propose a new learning method, "Generalized Learning Vector Quantization (GLVQ)," in which reference vectors are updated based on the steepest descent method in order to minimize the cost function . The cost function is determined so that the obtained learning rule satisfies the convergence condition. We prove that Kohonen's rule as used in LVQ does not satisfy the convergence condition and ...

Kohonen's self-organizing feature map (KSOFM) is an adaptive vector quantization (VQ) scheme for progressive code vector update. However, KSOFM approach belongs to unconstrained vector quantization, which suuers from exponential growth of the codebook. In this paper, a learning tree-structured vector quantization (LTSVQ) is presented for overcoming this drawback, which is based on competitive l...

We consider images of boar spermatozoa obtained with an optical phase-contrast microscope. Our goal is to automatically classify single sperm cells as acrosome-intact (class 1) or acrosome-reacted (class 2). Such classification is important for the estimation of the fertilization potential of a sperm sample for artificial insemination. We segment the sperm heads and compute a feature vector for...

We investigate the greedy version of the L-optimal vector quantization problem for an Rvalued random vector X ∈ L. We show the existence of a sequence (aN )N≥1 such that aN minimizes a 7→ ∥min1≤i≤N−1 |X−ai| ∧ |X−a| ∥∥ Lp (L-mean quantization error at level N induced by (a1, . . . , aN−1, a)). We show that this sequence produces L -rate optimal N -tuples a = (a1, . . . , aN ) (i.e. the L -mean q...

We propose a new algorithm for vector quantization:Average Competitive Learning Vector Quantization(ACLVQ). It is a rather simple modification of the classical Competitive Learning Vector Quantization(CLVQ). This new formulation gives us similar results for the quantization error to those obtained by the CLVQ and reduce considerably the computation time to achieve the optimal quantizer. We esta...

the proposed iafc neural networks have both stability and plasticity because theyuse a control structure similar to that of the art-1(adaptive resonance theory) neural network.the unsupervised iafc neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. this fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. the supervised iafc ...

We propose a rough classification system using Hierarchical Learning Vector Quantization (HLVQ) for large scale classification problems which involve many categories. HLVQ of proposed system divides categories hierarchically in the feature space, makes a tree and multiplies the nodes down the hierarchy. The feature space is divided by a few codebook vectors in each layer. The adjacent feature s...

The study focused on the machine learning analysis approaches to identify the adulteration of 9 kinds of edible oil qualitatively and answered the following three questions: Is the oil sample adulterant? How does it constitute? What is the main ingredient of the adulteration oil? After extracting the highperformance liquid chromatography (HPLC) data on triglyceride from 370 oil samples, we appl...

Traditional deep learning models are trained at a centralized server using data samples collected from users. Such often include private information, which the users may not be willing to share. Federated (FL) is an emerging approach train such without requiring share their data. FL consists of iterative procedure, where in each iteration copy model locally. The then collects individual updates...