Region Discovery Technology - Department of Computer Science
... building search engines that can navigate through millions of documents and return a ranked set of documents based on user interests and user feedback. Earth scientists are interested to have similar capabilities to find interesting regions on the planet earth based on knowledge that s stored in mul ...
... building search engines that can navigate through millions of documents and return a ranked set of documents based on user interests and user feedback. Earth scientists are interested to have similar capabilities to find interesting regions on the planet earth based on knowledge that s stored in mul ...
An Intelligent Assistant for the Knowledge Discovery Process
... We support the first claim by presenting in detail the design of an effective IDA for costsensitive classification, including a working prototype, describing how valid plans are enumerated based on an ontology that specifies the characteristics of the various component techniques. We show plans that ...
... We support the first claim by presenting in detail the design of an effective IDA for costsensitive classification, including a working prototype, describing how valid plans are enumerated based on an ontology that specifies the characteristics of the various component techniques. We show plans that ...
Data Mining - PhD in Information Engineering
... If x ≤ 1.2 then class = b If x > 1.2 and y ≤ 2.6 then class = b ...
... If x ≤ 1.2 then class = b If x > 1.2 and y ≤ 2.6 then class = b ...
Property Preservation in Reduction of Data Volume for
... data in V is discretized (required by the algorithm A). To remove this obstacle, either data in V’ needs to be discretized or algorithm A needs to be replaced by another algorithm that can process both discrete and continuous data. The first option is more logical because it does not limit the list ...
... data in V is discretized (required by the algorithm A). To remove this obstacle, either data in V’ needs to be discretized or algorithm A needs to be replaced by another algorithm that can process both discrete and continuous data. The first option is more logical because it does not limit the list ...
IJAI-6 - aut.upt.ro
... supervised machine learning methods, Naive Bayes, Maximum Entropy Model and Support Vector Machines. Their results show that machine learning techniques definitely outperform human-produced baselines. Additionally they found that machine learning approaches could not perform as well on sentiment cla ...
... supervised machine learning methods, Naive Bayes, Maximum Entropy Model and Support Vector Machines. Their results show that machine learning techniques definitely outperform human-produced baselines. Additionally they found that machine learning approaches could not perform as well on sentiment cla ...
Intrinsic Dimensional Outlier Detection in High
... HE goal of outlier detection, one of the fundamental data mining tasks, is to identify data objects that do not fit well in the general data distribution. Applications include areas as diverse as fraud detection, error elimination in scientific data, or sports data analysis. Examples of successful o ...
... HE goal of outlier detection, one of the fundamental data mining tasks, is to identify data objects that do not fit well in the general data distribution. Applications include areas as diverse as fraud detection, error elimination in scientific data, or sports data analysis. Examples of successful o ...
Adattarhaz
... Amikor kliens oldalról lekérdezés érkezik, egy meta-könyvtár segítségével a lekérdezés a heterogén adatbázis egy eleméhez kapcsolódó lekérdezésre fordítódik, és az egyes lekérdezések eredményei egy globális válasszá integrálódnak ...
... Amikor kliens oldalról lekérdezés érkezik, egy meta-könyvtár segítségével a lekérdezés a heterogén adatbázis egy eleméhez kapcsolódó lekérdezésre fordítódik, és az egyes lekérdezések eredményei egy globális válasszá integrálódnak ...
Decision Trees for Uncertain Data
... I. I NTRODUCTION Classification is a classical problem in machine learning and data mining[1]. Given a set of training data tuples, each having a class label and being represented by a feature vector, the task is to algorithmically build a model that predicts the class label of an unseen test tuple ...
... I. I NTRODUCTION Classification is a classical problem in machine learning and data mining[1]. Given a set of training data tuples, each having a class label and being represented by a feature vector, the task is to algorithmically build a model that predicts the class label of an unseen test tuple ...
Study of Density based Algorithms
... methods. Among many types of clustering algorithms density based algorithm is more efficient in detecting the clusters with varied density. Clustering analysis divides data into groups (Clusters) that are meaningful. If meaningful groups are goals, then cluster should capture the natural structure o ...
... methods. Among many types of clustering algorithms density based algorithm is more efficient in detecting the clusters with varied density. Clustering analysis divides data into groups (Clusters) that are meaningful. If meaningful groups are goals, then cluster should capture the natural structure o ...
A Fast Algorithm For Data Mining - SJSU ScholarWorks
... databases is an area of active research [Survey]. Some of the challenges common to algorithms for mining frequently occurring patterns in large data repositories are [Survey]: 1. Identifying the set (possibly, complete) of patterns that satisfy userspecified thresholds, such as, minsup 2. Minimize t ...
... databases is an area of active research [Survey]. Some of the challenges common to algorithms for mining frequently occurring patterns in large data repositories are [Survey]: 1. Identifying the set (possibly, complete) of patterns that satisfy userspecified thresholds, such as, minsup 2. Minimize t ...
K-nearest neighbors algorithm
In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.Both for classification and regression, it can be useful to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required.A shortcoming of the k-NN algorithm is that it is sensitive to the local structure of the data. The algorithm has nothing to do with and is not to be confused with k-means, another popular machine learning technique.