The dramatic growth in the number and size of on-line information sources has fueled increasing research interest in the incremental subspace learning problem. In this paper, we propose an incremental supervised subspace learning algorithm, called Incremental Inter-class Scatter (IIS) algorithm. Unlike traditional batch learners, IIS learns from a stream of training data, not a set. IIS overcom...

The present paper deals with a systematic study of incremental learning algorithms. The general scenario is as follows. Let c be any concept; then every innnite sequence of elements exhausting c is called positive presentation of c. An algorith-mic learner successively takes as input one element of a positive presentation as well as its previously made hypothesis at a time, and outputs a new hy...

First, this paper recalls a recently introduced method of adaptive monitoring of dynamical systems and presents the most recent extension with a multiscale-enhanced approach. Then, it is shown that this concept of real-time data monitoring establishes a novel non-Shannon and non-probabilistic concept of novelty quantification, i.e., Entropy of Learning, or in short the Learning Entropy. This no...

In this paper, an algorithm for datadriven incremental learning of fuzzy basis function networks is presented. A modified version of vector quantization is exploited for rule evolution and incremental learning of the rules’ antecedent parts. Antecedent learning is connected in a stable manner with a recursive learning of rule consequent functions with linear parameters. The paper is concluded w...

We present a category learning vector quantization (cLVQ) approach for incremental and life-long learning of multiple visual categories where we focus on approaching the stability-plasticity dilemma. To achieve the life-long learning ability an incremental learning vector quantization approach is combined with a category-specific feature selection method in a novel way to allow several metrical...

This paper presents a strategy of adaptive online sketchy shape recognition. The inputting sketchy shapes are recognized online by means of a modified Support Vector Machine (SVM) incremental learning classifier. All classified results evaluated by user are collected and some important samples are selected according to their distances to the hyper-plane of the SVM-classifier. The classifier can...

This report points out the role of sequences of samples for training an incremental learning method. We define characteristics of incremental learning methods to describe the influence of sample ordering on the performance of a learned model. Different types of experiments evaluate these properties for two different datasets and two different incremental learning methods. We show how to find se...

In the paper we propose a novel method for incremental visual learning by combining reconstructive and discriminative subspace methods. This is achieved by embedding LDA learning and classification into the incremental PCA framework. The combined subspace consists of a truncated PCA subspace and a few additional basis vectors that encompass the discriminative information, which would be lost by...

In this paper we discuss combining incremental learning and incremental recognition to classify patterns consisting of multiple objects, each represented by multiple spatiotemporal features. Importantly the technique allows for ambiguity in terms of the positions of the start and finish of the pattern. This involves a progressive classification which considers the data at each time instance in ...

We proposed a method of incremental projection learning which provides exactly the same generalization capability as that obtained by batch projection learning in the previous paper. However, properties of the method have not yet been investigated. In this paper, we analyze its properties from the following aspects: First, it is shown that some of the training data which is regarded as redundan...