In this paper we describe work relating to classification of web documents using a graph-based model instead of the traditional vector-based model for document representation. We compare the classification accuracy of the vector model approach using the kNearest Neighbor (k-NN) algorithm to a novel approach which allows the use of graphs for document representation in the k-NN algorithm. The pr...

We show that when estimating a nonparametric regression model, the knearest-neighbor nonparametric estimation method has the ability to remove irrelevant variables provided one uses a product weight function with a vector of smoothing parameters, and the least squares cross validation method is used to select the smoothing parameters. Simulation results are consistent with our theoretical analy...

Adverse effects of drugs (AEDs) continue to be a major cause of drug withdrawals in both development and postmarketing. While liver-related AEDs are a major concern for drug safety, there are few in silico models for predicting human liver toxicity for drug candidates. We have applied the quantitative structure-activity relationship (QSAR) approach to model liver AEDs. In this study, we aimed t...

The main weakness of the k-Nearest Neighbor algorithm in face recognition is calculating the distance and sort all training data on each prediction which can be slow if there are a large number of training instances. This problem can be solved by utilizing the priority k-d tree search to speed up the process of k-NN classification. This paper proposes a method for student attendance systems in ...

Pemanfaatan internet dalam dunia pariwisata sangat membantu wisatawan merencanakan perjalanan termasuk mengeksplor daerah wisata lokal. Keberadaan media khusus seperti fasilitas web smart tourism untuk mempublikasikan tempat-tempat lokal dan yang ada di tentu akan para stakeholder tersebut wisatawan. Untuk memudahkan sebagai pengguna mengakses informasi mengenai dikunjungi diperlukan adanya sis...

Lazy classifiers store all of the training samples and do not build a classifier until a new sample needs to be classified. It differs from eager classifiers, such as decision tree induction, which build a general model (such as a decision tree) before receiving new samples. K-nearest neighbor (KNN) classification is a typical lazy classifier. Given a set of training data, a knearest neighbor c...

In [1], Aha et al. introduced a framework and methodology for machine learning, which is instances-based learning. They defined a set of rules for nearest neighbor algorithms and extended them. These algorithms are still in use for knearest neighbor classifier, which is very much related with my topic “similarity search”. In [2], Galper et al. described the similarity search and proposed a dyna...

Accurate, up-to-date and accessible information on the state of coral reef ecosystem is necessary for informed and effective management of these important marine resources. However, environments containing these habitats are challenging to map due to their remoteness, extent and costs of monitoring. In this research, the capabilities of satellite remote sensing techniques combined with in situ ...

The k-nearest neighbor rule is one of the most attractive pattern classification algorithms. In practice, the value of k is usually determined by the cross-validation method. In this work, we propose a new method that locally determines the number of nearest neighbors based on the concept of statistical confidence. We define the confidence associated with decisions that are made by the majority...