It is shown that the conventional sampling schemes are of limited use for conversion of monotonic long-time-interval and wide-frequency-band data of relaxation experiments, which can be measured over many decades of time or frequency. The problem of sampling is considered in combination with designing the discrete-time algorithms for relaxation data conversion has been generalized as a convolut...

In this paper we study a dynamic pricing problem, where a firm offers a product to be sold over a fixed time horizon. The firm has a given initial inventory level, but there is uncertainty about the demand for the product in each time period. The objective of the firm is to determine a robust and dynamic pricing strategy that maximizes revenue over the entire selling season. We develop a tracta...

Traditional graph sampling methods reduce the size of a large network via uniform sampling of nodes from the original network. The sampled network can be used to estimate the topological properties of the original network. However, in some application domains (e.g., disease surveillance), the goal of sampling is also to help identify a specified category of nodes (e.g., affected individuals) in...

Data mining is a complex process that aims to derive an accurate predictive model starting from a collection of data. Traditional approaches assume that data are given in advance and their quality, size and structure are independent parameters. In this paper we argue that an extended vision of data mining should include the step of data acquisition as part of the overall process. Moreover the s...

Sampling is the common practice involved in academic and industry efforts on recommendation algorithm evaluation and selection. Experimental analysis often uses a subset of the entire useritem interaction data available in the operational recommender system, often derived by including all transactions associated with a subset of uniformly randomly selected users. Our paper formally studies the ...

In the literature of supervised learning, most existing studies assume that the labels provided by the labelers are deterministic, which may introduce noise easily in many real-world applications. In many applications like crowdsourcing, however, many labelers may simultaneously label the same group of instances and thus the label of each instance is associated with a probability. Motivated by ...

Data reduction is an important issue in the field of data mining. This article describes a new method for selecting a subset of data from a large dataset. A simplified chi-square criterion is proposed for measuring the goodness-of-fit between the distributions of the reduced and full data sets. Under this criterion, the data reduction problem can be formulated as a binary quadratic program and ...

In data mining, sampling may be used as a technique for reducing the amount of data presented to a data mining algorithm. Other strategies for data reduction include dimension reduction, data compression, and discretisation. For sampling, the aim is to draw, from a database, a random sample, which has the same characteristics as the original database. This chapter looks at the sampling methods ...