نتایج جستجو برای: online learning algorithm
تعداد نتایج: 1456670 فیلتر نتایج به سال:
This paper proposes a new method for online secondary path modeling in feedback active noise control (ANC) systems. In practical cases, the secondary path is usually time-varying. For these cases, online modeling of secondary path is required to ensure convergence of the system. In literature the secondary path estimation is usually performed offline, prior to online modeling, where in the prop...
In this paper, we propose a novel online learning algorithm, SpCoSLAM 2.0 for spatial concepts and lexical acquisition with higher accuracy and scalability. In previous work, we proposed SpCoSLAM as an online learning algorithm based on the Rao–Blackwellized particle filter. However, this conventional algorithm had problems such as the decrease of the estimation accuracy due to the influence of...
Online learning algorithms are typically fast, memory efficient, and simple to implement. However, many common learning problems fit more naturally in the batch learning setting. The power of online learning algorithms can be exploited in batch settings by using online-to-batch conversions, techniques which build a new batch algorithm from an existing online algorithm. We first give a unified o...
Online co-learning system plays a great role in undergraduate education recent years. Efficient and timely tutor support makes the co-learning more efficient. This paper is intends to present a method to evaluate the quality of tutor support for an online co-learning system. The online co-learning system is briefly introduced and the definition of the evaluation criteria is demonstrated. The al...
We introduce an online framework for discriminative learning problems over hidden structures, where we learn both the latent structure and the classifier for a supervised learning task. Previous work on leveraging latent representations for discriminative learners has used batch algorithms that require multiple passes though the entire training data. Instead, we propose an online algorithm that...
The recently proposed Bayesian approach to online learning is applied to learning a rule deened as a noisy single layer perceptron with either continuous or binary weights. In the Bayesian online approach the exact posterior distribution is approximated by a simpler paramet-ric posterior that is updated online as new examples are incorporated to the dataset. In the case of continuous weights, t...
In this paper, reinforcement learning algorithms are applied to a foraging task, expressed as a control composition problem. The domain used is a simulated world in which a variety of creatures (agents) live and interact, reacting to stimuli and to each other. In such dynamic, uncertain environments , fast adaptation is important, and there is a need for new architectures that facilitate on-lin...
This paper presents an online algorithm for dependency parsing problems. We propose an adaptation of the passive and aggressive online learning algorithm to the dependency parsing domain. We evaluate the proposed algorithms on the 2007 CONLL Shared Task, and report errors analysis. Experimental results show that the system score is better than the average score among the participating systems.
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