While classic machine learning paradigms assume training and test data are generated from the same process, domain adaptation addresses the more realistic setting in which the learner has large quantities of labeled data from some source task but limited or no labeled data from the target task it is attempting to learn. In this work, we give the first formal analysis showing that using active l...

Complex networks, such as biological, social, and communication networks, often entail uncertainty, and thus, can be modeled as probabilistic graphs. Similar to the problem of similarity search in standard graphs, a fundamental problem for probabilistic graphs is to efficiently answer k-nearest neighbor queries (k-NN), which is the problem of computing the k closest nodes to some specific node....

We present a detailed analysis of the connecting-nearest-neighbors model by Vázquez [Phys. Rev. E 67, 056104 (2003)]. We show that the degree distribution follows a power law, but the scaling exponent can vary with the parameter setting. Moreover, the correspondence of the growing version of the connecting-nearest-neighbors model to the particular random walk model and recursive search model is...

TECHNIQUES FOR EFFICIENT K-NEAREST NEIGHBOR SEARCHING IN NON-ORDERED DISCRETE AND HYBRID DATA SPACES

Credit scoring has gained more and more attentions both in academic world and the business community today. Many modeling techniques have been developed to tackle the credit scoring tasks. Credit scoring models have been widely used by financial institutions to determine if loan customers belong to either a good applicant group or a bad applicant group. The advantages of using credit scoring mo...

The K Nearest Neighbors classification method assigns to an unclassified observation the class which obtains the best results after a voting criteria is applied among the observation’s K nearest, previously classified points. In a validation process the optimal K is selected for each database and all the cases are classified with this K value. However the optimal K for the database does not hav...

In this study, we attempt to distinguish between acute myeloid leukemia (AML) and acute lymphoid leukemia (ALL) using microarray gene expression data. Bayes’ classification is used with three different density estimation techniques: Parzen, k nearest neighbors(k-NN), and a new hybrid method, called k-neighborhood Parzen (k-NP), that combines properties of the other two. The classifiers are appl...

Patent management is increasingly important for organizations to sustain their competitive advantage. The classification of patents is essential for patent management and industrial analysis. In this study, we propose a novel patent network-based classification method to analyze query patents and predict their classes. The proposed patent network, which contains various types of nodes that repr...

We present two related contributions of independent interest: (1) high-probability finite sample rates for k-NN density estimation, and (2) practical mode estimators – based on k-NN – which attain minimax-optimal rates under surprisingly general distributional conditions.

Many researches have been devoted to learn a Mahalanobis distance metric, which can effectively improve the performance of kNN classification. Most approaches are iterative and computational expensive and linear rigidity still critically limits metric learning algorithm to perform better. We proposed a computational economical framework to learn multiple metrics in closed-form.