Yishay Mansourt Vie sho,v the convergence of tV/O deterministic variants of Qlearning. The first is the widely used optimistic Q-learning, which initializes the Q-values to large initial values and then follows a greedy policy with respect to the Q-values. We show that setting the initial value sufficiently large guarantees the converges to an Eoptimal policy. The second is a new and novel algo...

An incremental learning algorithm based on support vector machine was proposed to process large-scale data or data generated in batches. Initial goal concept learnt by standard support vector machine algorithm was updated by an updating model. Compared with the existing incremental learning algorithms, this algorithm can achieve the incremental inverse process and the training time is in invers...

Multi-class supervised learning systems require the knowledge of the entire range of labels they predict. Often when learnt incrementally, they suffer from catastrophic forgetting. To avoid this, generous leeways have to be made to the philosophy of incremental learning that either forces a part of the machine to not learn, or to retrain the machine again with a selection of the historic data. ...

As business information quickly varies with time, the extraction of knowledge from the related dynamically changing database is vital for business decision making. For an incremental learning optimization on knowledge discovery, a new incremental matrix describes the changes of the system. An optimization incremental algorithm induces interesting knowledge when the object set varies over time. ...

This paper describes PISCES 1.2E, a system for incremental learning of probabilistic rules. PISCES is efficiently incremental in the sense that both its processing time per instance and its memory usage are independent of the number of training instances. Classification accuracy alone does not provide a sufficient measure of performance for probabilistic classifiers. Additional measures include...

This paper proposes a new neocognitron that accepts incremental learning, without giving a severe damage to old memories or reducing learning speed. The new neocognitron uses a competitive learning, and the learning of all stages of the hierarchical network progresses simultaneously. To increase the learning speed, conventional neocognitrons of recent versions sacrificed the ability of incremen...

As linguistic competence so clearly illustrates, processing sequences of events is a fundamental aspect of human cognition. For this reason perhaps, sequence learning behavior currently attracts considerable attention in both cognitive psychology and computational theory. In typical sequence learning situations, participants are asked to react to each element of sequentially structured visual s...

a persistent challenge in large public health programs is the introduction of new or improved program content. the solution usually followed is to ensure adequate durations of class-room training with the hope that they will retain such new teaching and change their ways of working. Huge numbers of such training activities interrupt large programs, and the negligible changes in program outcomes...

We propose a new approach for video event learning. The only hypothesis is the availability of tracked object attributes. The approach incrementally aggregates the attributes and reliability information of tracked objects to learn a hierarchy of state and event concepts. Simultaneously, the approach recognises the states and events of the tracked objects. This approach proposes an automatic bri...

The paper provides a systematic study of incremental learning algorithms. The general scenario is as follows. Let c be any concept; then every in nite sequence of elements exhausting c is called positive presentation of c. An algorithmic learner takes as input one element of a positive presentation and its previously made hypothesis at a time, and outputs a new hypothesis about the target conce...