Full-Text - International Journal of Computer Science Issues
... distinguished from supervised learning in that the learner is given only unlabeled examples. Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning ...
... distinguished from supervised learning in that the learner is given only unlabeled examples. Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning ...
IS 579— Business Intelligence and Data Mining
... identify problems and opportunities in their companies and apply these techniques. Special attention would be given to existing real-world applications that make use of data mining techniques. Students are expected to understand the basic concepts and their applicability, but are not expected to do ...
... identify problems and opportunities in their companies and apply these techniques. Special attention would be given to existing real-world applications that make use of data mining techniques. Students are expected to understand the basic concepts and their applicability, but are not expected to do ...
Multi-represented kNN-Classification for Large Class Sets
... 14,000 classes and biometric databases will have to identify one special person among thousands of people. Though this problem is not directly connected to the complexity of the given data objects, it often co-occurs in the same application and should therefore be considered when selecting the class ...
... 14,000 classes and biometric databases will have to identify one special person among thousands of people. Though this problem is not directly connected to the complexity of the given data objects, it often co-occurs in the same application and should therefore be considered when selecting the class ...
An Introduction to Support Vector Machines for Data Mining
... be easy to compute, well-defined and span a sufficiently rich hypothesis space7. A common approach is to define a positive definite kernel that corresponds to a known classifier such as a Gaussian RBF, two-layer MLP or polynomial classifier. This is possible since Mercer’s theorem states that any po ...
... be easy to compute, well-defined and span a sufficiently rich hypothesis space7. A common approach is to define a positive definite kernel that corresponds to a known classifier such as a Gaussian RBF, two-layer MLP or polynomial classifier. This is possible since Mercer’s theorem states that any po ...
lect1 - University of South Carolina
... Fundamentals of Algorithmic Problem Solving 1. Understanding the problem 2. Ascertaining the capabilities of a computational device Random-access machine (RAM) sequential algorithms 3. Choose between exact and approximate problem solving 4. Deciding on appropriate data structure 5. Algorithm desi ...
... Fundamentals of Algorithmic Problem Solving 1. Understanding the problem 2. Ascertaining the capabilities of a computational device Random-access machine (RAM) sequential algorithms 3. Choose between exact and approximate problem solving 4. Deciding on appropriate data structure 5. Algorithm desi ...
Learning intrusion detection: supervised or unsupervised?
... fixing a set of false-positive rate values of interest and computing the means and the standard deviations of true-positive rate values over all runs for the values of interest. The supervised algorithms in general exhibit excellent classification accuracy on the data with known attacks. The best re ...
... fixing a set of false-positive rate values of interest and computing the means and the standard deviations of true-positive rate values over all runs for the values of interest. The supervised algorithms in general exhibit excellent classification accuracy on the data with known attacks. The best re ...
Predictive Analytics in Healthcare System Using Data Mining
... data become more and more difficult to manage in terms of volume, variety and velocity [3]. This gave birth to a new domain named big data. In 2008, Gartner used for the first time the term "Big Data", in reference to the explosion of digital data and quoted it will impact the way we work [4]. "Big ...
... data become more and more difficult to manage in terms of volume, variety and velocity [3]. This gave birth to a new domain named big data. In 2008, Gartner used for the first time the term "Big Data", in reference to the explosion of digital data and quoted it will impact the way we work [4]. "Big ...
Partition Algorithms– A Study and Emergence of Mining Projected
... In hierarchical clustering the data are not partitioned into a particular cluster in a single step. Instead, a series of partitions takes place, which may run from a single cluster containing all objects to n clusters each containing a single object [12]. Hierarchical Clustering is subdivided into a ...
... In hierarchical clustering the data are not partitioned into a particular cluster in a single step. Instead, a series of partitions takes place, which may run from a single cluster containing all objects to n clusters each containing a single object [12]. Hierarchical Clustering is subdivided into a ...
Study of Hybrid Genetic algorithm using Artificial Neural Network in
... Neural Network is used for the classification of diseases based on the features of the patients. A stroke is the sudden death of brain cells in a localized area due to inadequate blood flow. The sudden death of brain cells due to lack of oxygen, caused by blockage of blood flow or rupture of an arte ...
... Neural Network is used for the classification of diseases based on the features of the patients. A stroke is the sudden death of brain cells in a localized area due to inadequate blood flow. The sudden death of brain cells due to lack of oxygen, caused by blockage of blood flow or rupture of an arte ...
Embedded Algorithm in Hardware: A Scalable Compact Genetic
... Distribution Algorithm and Block-based Neural Network as an Evolvable Hardware", IEEE Congress on Evolutionary Computation, Hong Kong, June 1-6, 2008, pp.3365-3372. Jewajinda, Y. and Chongstitvatana, P., "A Cooperative Approach to Compact Genetic Algorithm for Evolvable Hardware", IEEE World Congres ...
... Distribution Algorithm and Block-based Neural Network as an Evolvable Hardware", IEEE Congress on Evolutionary Computation, Hong Kong, June 1-6, 2008, pp.3365-3372. Jewajinda, Y. and Chongstitvatana, P., "A Cooperative Approach to Compact Genetic Algorithm for Evolvable Hardware", IEEE World Congres ...
Cross Lingual Sentiment Analysis and Opinion Mining from User
... Unigram method reached 80% accuracy. ...
... Unigram method reached 80% accuracy. ...
C. Naïve Bayes - Academic Science,International Journal of
... and criminals. However crime investigation process needs to be faster and efficient. As large amount of information is collected during crime investigation, data mining is an approach which can be useful in this perspective. Data mining is a process that extracts useful information from large amount ...
... and criminals. However crime investigation process needs to be faster and efficient. As large amount of information is collected during crime investigation, data mining is an approach which can be useful in this perspective. Data mining is a process that extracts useful information from large amount ...
Clustering high-dimensional data derived from Feature Selection
... clustering based feature subset selection algorithm is used. The algorithm involves (i) removing irrelevant features, (ii) constructing clusters from the relevant features, and (iii) removing redundant features and selecting representative features. It is an effective way for reducing dimensionality ...
... clustering based feature subset selection algorithm is used. The algorithm involves (i) removing irrelevant features, (ii) constructing clusters from the relevant features, and (iii) removing redundant features and selecting representative features. It is an effective way for reducing dimensionality ...
slides
... Shannon’s Entropy • If we are tossing a coin and want to send a sequence of tosses – If heads and tails have the same probability it would take one bit per toss – If heads have a very small probability compared to tails we could just send a list of the heads and say that everything else is a tail • ...
... Shannon’s Entropy • If we are tossing a coin and want to send a sequence of tosses – If heads and tails have the same probability it would take one bit per toss – If heads have a very small probability compared to tails we could just send a list of the heads and say that everything else is a tail • ...
Knowledge Discovery and Data Mining: Concepts and Fundamental
... interviews). In the case of computer-aided analysis, the analyst had t o enter the colIected data into a statistical computer ~ackageor an electronic spreadsheet. Due to the high cost of data collection, people learned to make decisions based on limited information. However, since the information-ag ...
... interviews). In the case of computer-aided analysis, the analyst had t o enter the colIected data into a statistical computer ~ackageor an electronic spreadsheet. Due to the high cost of data collection, people learned to make decisions based on limited information. However, since the information-ag ...
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.