let g be a (p, q) graph. let k be an integer with 2 ≤ k ≤ p and f from v (g) to the set {1, 2, . . . , k} be a map. for each edge uv, assign the label |f(u) − f(v)|. the function f is called a k-difference cordial labeling of g if |νf (i) − vf (j)| ≤ 1 and |ef (0) − ef (1)| ≤ 1 where vf (x) denotes the number of vertices labelled with x (x ∈ {1, 2 . . . , k}), ef (1) and ef (0) respectively den...

Interleaving is used for error-correcting on a bursty noisy channel. Given a graph describing the topology of the channel, we label the vertices of so that each label-set is sufficiently sparse. Interleaving scheme corrects for any error burst of size at most ; it is a labeling where the distance between any two vertices in the same label-set is at least . We consider interleaving schemes on in...

Many real-world applications require multi-label classification where multiple target labels are assigned to each instance. In multi-label classification, there exist the intrinsic correlations between the labels and features. These correlations are beneficial for multi-label classification task since they reflect the coexistence of the input and output spaces that can be exploited for predicti...

Stratified sampling is a sampling method that takes into account the existence of disjoint groups within a population and produces samples where the proportion of these groups is maintained. In single-label classification tasks, groups are differentiated based on the value of the target variable. In multi-label learning tasks, however, where there are multiple target variables, it is not clear ...

Multi-label classification has rapidly attracted interest in the machine learning literature, and there are now a large number and considerable variety of methods for this type of learning. We present Meka: an open-source Java framework based on the well-known Weka library. Meka provides interfaces to facilitate practical application, and a wealth of multi-label classifiers, evaluation metrics,...

We study the label complexity of pool-based active learning in the agnostic PAC model. Specifically, we derive general bounds on the number of label requests made by the A algorithm proposed by Balcan, Beygelzimer & Langford (Balcan et al., 2006). This represents the first nontrivial general-purpose upper bound on label complexity in the agnostic PAC model.

With the diversification of the TC task, to construct multi-label classifier is often more in line with the needs of practical applications. However, in a multi-label classification task, each document often corresponds to more than one class label. In this chapter, we will construct a compound classification framework which may transform a multi-label classification task into several single la...

The Drug Facts Label is designed to guide consumers in comparing nonprescription drugs. Undergraduates studied and recalled drug facts for three analgesic or non-analgesic labels using Drug Facts Label headings as retrieval cues. They then studied and recalled drug facts from an aspirin label. Aspirin recall was greater when the prior labels were analgesics, but prior-label intrusion errors wer...

A common approach to solving multi-label learning problems is to use problem transformation methods and dichotomizing classifiers as in the pair-wise decomposition strategy. One of the problems with this strategy is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming, especially in learning problems with a large number of label...