* U. Johansson and R. König are equal contributors to this paper. Abstract Standard kNN suffers from two major deficiencies, both related to the parameter k. First of all, it is well-known that the parameter value k is not only extremely important for the performance, but also very hard to estimate beforehand. In addition, the fact that k is a global constant, totally independent of the particu...

The fingerprint-based wireless local area network (WLAN) positioning has gained significant interest in recent years. Indoor localization methods based on WLAN and RSS with advantage of low cost are most widely used. In this paper, we propose an improved indoor positioning method based on Affinity Propagation (AP) algorithm to obtain better accuracy: k coordinate points measured through KNN alg...

Probabilistic k-nearest neighbour (PKNN) classification has been introduced to improve the performance of original k-nearest neighbour (KNN) classification algorithm by explicitly modelling uncertainty in the classification of each feature vector. However, an issue common to both KNN and PKNN is to select the optimal number of neighbours, k. The contribution of this paper is to incorporate the ...

A processing algorithm to classify hyperspectral images from an imaging spectroscopic sensor is investigated in this paper. In this research two approaches are followed. First, the feasibility of an analysis scheme consisting of spectral feature extraction and classification is demonstrated. Principal component analysis (PCA) is used to perform data dimensionality reduction while the spectral i...

OBJECTIVE There may be great potential for using computer-modeling techniques and machine-learning algorithms in clinical decision making, if these can be shown to produce results superior to clinical protocols currently in use. We aim to explore the potential to use an automatic, data-driven, machine-learning algorithm in clinical decision making. STUDY DESIGN AND SETTING Using a database co...

Medical diagnosis research has recently focused on feature selection techniques due to the availability of multiple variables in medical datasets. Wrapper-based approaches have shown promise providing faster and more cost-effective predictors. However, selecting most relevant features from datasets increase disease classification accuracy remains a challenging issue. To address this challenge, ...

Advances in mobile technologies and devices has changed the way users interact with devices and other users. These new interaction methods and services are offered by the help of intelligent sensing capabilities, using context, location and motion sensors. However, indoor location sensing is mostly achieved by utilizing radio signal (Wi-Fi, Bluetooth, GSM etc.) and nearest neighbor identificati...

This paper reports our experiments for TRECVID 2010 task: Semantic Indexing. We present two approaches namely, Affective and Holistic. In the first approach, we have used combination of affective features from image, video and audio trained with neural network algorithm. Image features employed are color histogram and face detection from the keyframe. The number of face is also used in one of t...

Recently, due to the proliferation and no requirement for extra infrastructure investments, WLAN indoor positioning has received more and more attention. As the most famous pattern matching based method, KNN algorithm is widely implemented. However, the extensive computing and poor positioning accuracy trouble the practical application. This paper proposes a novel two-step WLAN indoor positioni...