Causal structure learning algorithms have focused on learning in ”batch-mode”: i.e., when a full dataset is presented. In many domains, however, it is important to learn in an online fashion from sequential or ordered data, whether because of memory storage constraints or because of potential changes in the underlying causal structure over the course of learning. In this paper, we present TDSL,...

Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch learning setting, which requires the entire training data to be made available prior to the learning task. This is not scalable for many real-world scenarios where new data arrives sequentially in a stream form. We aim to address an open challenge of “Online Deep Learning” (ODL) for learning DNNs on the fly in an on...

Social networking services like Twitter have been playing an import role in people’s daily life since it supports new ways of communicating effectively and sharing information. The advantages of these social network services enable them rapidly growing. However, the rise of social network services is leading to the increase of unwanted, disruptive information from spammers, malware discriminato...

In speedup learning problems where full descriptions of operators are known both explanation based learning EBL and reinforcement learning RL methods can be applied This paper shows that both methods involve fundamentally the same process of propagating information backward from the goal toward the starting state Most RL methods perform this propagation on a state by state basis while EBL metho...

Pattern reordering is proposed as an alternative to sequential and batch processing for online category learning. Upon detecting that the categorization of a new input pattern is ambiguous, the input is postponed for a predefined time, after which it is reexamined and categorized for good. This approach is shown to improve the categorization performance over purely sequential processing, while ...

Near real time ETL deviates from the traditional conception of data warehouse refreshment, which is performed off-line in a batch mode, and adopts the strategy of propagating changes that take place in the sources towards the data warehouse to the extent that both the sources and the warehouse can sustain the incurred workload. In this article, we review the state of the art for both convention...