In current dependency parsing models, conventional features (i.e. base features) defined over surface words and part-of-speech tags in a relatively high-dimensional feature space may suffer from the data sparseness problem and thus exhibit less discriminative power on unseen data. In this paper, we propose a novel semi-supervised approach to addressing the problem by transforming the base featu...

In machine learning classification and recognition are crucial tasks. Any object is recognized with the help of features associated with it. Among many features only some leads to classify object correctly. Feature selection is useful technique to detect such specific features. Feature selection is a process of selecting subset of features to reduce number of features (dimensionality reduction)...

Semi-supervised clustering algorithms are increasingly employed for discovering hidden structure in data with partially labelled patterns. In order to make the clustering approach useful and acceptable to users, the information provided must be simple, natural and limited in number. To improve recognition capability, we apply an effective feature enhancement procedure to the entire data-set to ...

With the continued and relentless growth in dataset sizes in recent times, feature or attribute selection has become a necessary step in tackling the resultant intractability. Indeed, as the number of dimensions increases, the number of corresponding data instances required in order to generate accurate models increases exponentially. Fuzzy-rough set-based feature selection techniques offer gre...

Resection or ablation of tumours is one treatment available for liver cancer. This delicate operation consists of removing the tumour(s) and surrounding healthy tissues. The surgery is complicated by the fact that major blood vessels are present in the liver: the surgeon must proceed cautiously. Computer Tomography (CT) scans are used to diagnose the presence of tumours in the liver but also to...

We have developed a semi-automated procedure for generating correctly located 3D tree objects from overhead imagery. Cross-platform software partitions arbitrarily large, geocorrected and geolocated imagery into manageable sub-images. The user manually selects tree areas from one or more of these sub-images. Samples are taken from these areas, and color statistics are computed. Tree areas are d...

Clustering is the process which is used to assign a set of n objects into clusters(groups). Dimensionality reduction techniques help in increasing the accuracy of clustering results by removing redundant and irrelevant dimensions. But, in most of the situations, objects can be related in different ways in different subsets of the dimensions. Dimensionality reduction tends to get rid of such rel...

Feature selection is an important task in effective data mining. A new challenge to feature selection is the socalled “small labeled-sample problem” in which labeled data is small and unlabeled data is large. The paucity of labeled instances provides insufficient information about the structure of the target concept, and can cause supervised feature selection algorithms to fail. Unsupervised fe...

In many data analysis tasks, one is often confronted with the problem of selecting features from very high dimensional data. Most existing feature selection methods focus on ranking individual features based on a utility criterion, and select the optimal feature set in a greedy manner. However, the feature combinations found in this way do not give optimal classification performance, since they...

The body of residential complexes is affected by social interactions between residents over time. Of the most important places for social communications are bazaars as the backbone of Iranian cities. Ancient bazaar of Babol in north of Iran contains natural features which makes a special sense of place. This essay tries to answer to how concept of sense of place is presented and what are factor...