Wyniki wyszukiwania - Biblioteka Nauki

1

On training deep neural networks using a streaming approach

100%

Duda P. , Jaworski M. , Cader A. , Wang L.

Journal of Artificial Intelligence and Soft Computing Research

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2020

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tom Vol. 10, No. 1

15--26

EN

In recent years, many deep learning methods, allowed for a significant improvement of systems based on artificial intelligence methods. Their effectiveness results from an ability to analyze large labeled datasets. The price for such high accuracy is the long training time, necessary to process such large amounts of data. On the other hand, along with the increase in the number of collected data, the field of data stream analysis was developed. It enables to process data immediately, with no need to store them. In this work, we decided to take advantage of the benefits of data streaming in order to accelerate the training of deep neural networks. The work includes an analysis of two approaches to network learning, presented on the background of traditional stochastic and batch-based methods.

2

Survival analysis on data streams: Analyzing temporal events in dynamically changing environments

100%

Shaker A. , Hüllermeier E.

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tom 24

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nr 1

199-212

EN

In this paper, we introduce a method for survival analysis on data streams. Survival analysis (also known as event history analysis) is an established statistical method for the study of temporal “events” or, more specifically, questions regarding the temporal distribution of the occurrence of events and their dependence on covariates of the data sources. To make this method applicable in the setting of data streams, we propose an adaptive variant of a model that is closely related to the well-known Cox proportional hazard model. Adopting a sliding window approach, our method continuously updates its parameters based on the event data in the current time window. As a proof of concept, we present two case studies in which our method is used for different types of spatio-temporal data analysis, namely, the analysis of earthquake data and Twitter data. In an attempt to explain the frequency of events by the spatial location of the data source, both studies use the location as covariates of the sources.

3

Data warehouse for event streams violating rules

86%

Czejdo B. D. , Ferragut E. M. , Goodall J. R. , Laska J.

Foundations of Computing and Decision Sciences

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2013

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tom Vol. 38, No. 2

87--96

EN

In this presentation, we discuss how a data warehouse can support situational awareness and data forensic needs for investigation of event streams violating rules. The data warehouse for event streams can contain summary tables showing rule violation on different aggregation level. We will introduce the classification of rules and the concept of a general aggregation graph for defining various classes of rules violation and their relationships. The data warehouse system containing various rule violation aggregations will allow the data forensics experts to have the ability to “drill-down” into event data across different data warehouse dimensions. The event stream real-time processing and other software modules can also use the summarizations to discover if current events bursts satisfy rules by comparing them with historic event bursts.

4

Incremental rule-based learners for handling concept drift: an overview

72%

Deckert M.

Foundations of Computing and Decision Sciences

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2013

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tom Vol. 38, No. 1

35--65

EN

Learning from non-stationary environments is a very popular research topic. There already exist algorithms that deal with the concept drift problem. Among them there are online or incremental learners, which process data instance by instance. Their knowledge representation can take different forms such as decision rules, which have not received enough attention in learning with concept drift. This paper reviews incremental rule-based learners designed for changing environments. It describes four of the proposed algorithms: FLORA, AQ11-PM+WAH, FACIL and VFDR. Those four solutions can be compared on several criteria, like: type of processed data, adjustment to changes, type of the maintained memory, knowledge representation, and others.

5

Prediction of the shoppers loyalty with aggregated data streams

51%

Nikulin V.

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2016

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tom Vol. 6, No. 2

69--79

EN

Consumer brands often offer discounts to attract new shoppers to buy their products. The most valuable customers are those who return after this initial incentive purchase. With enough purchase history, it is possible to predict which shoppers, when presented an offer, will buy a new item. While dealing with Big Data and with data streams in particular, it is a common practice to summarize or aggregate customers’ transaction history to the periods of few months. As an outcome, we compress the given huge volume of data, and transfer the data stream to the standard rectangular format. Consequently, we can explore a variety of practically or theoretically motivated tasks. For example, we can rank the given field of customers in accordance to their loyalty or intension to repurchase in the near future. This objective has very important practical application. It leads to preferential treatment of the right customers. We tested our model (with competitive results) online during Kaggle-based Acquire Valued Shoppers Challenge in 2014.