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Automating opinion analysis in film reviews: the case
Automating opinion analysis in film reviews: the case

... features. Then the system classifies sentences as negative or positive by determining the dominant orientation of the opinion words of the sentence. The result of the comparison between two products is given in the form of diagram with features on X-coordinate and opinions polarity on Y-coordinate. ...
Identifying and Overcoming Common Data Mining Mistakes
Identifying and Overcoming Common Data Mining Mistakes

... Faced with the potentially daunting task of investigating all of their data, users often want to know which variables to use for a given model. This type of thinking has at least one inherent problem: it relies on the existence of some common subset of traits that can be used to satisfactorily model ...
Identifying and Overcoming Common Data Mining Mistakes
Identifying and Overcoming Common Data Mining Mistakes

Text Summarization using PSO
Text Summarization using PSO

26 Optimal Bounds for Johnson-Lindenstrauss
26 Optimal Bounds for Johnson-Lindenstrauss

Natural Language Model Re-usability for Scaling to Different Domains
Natural Language Model Re-usability for Scaling to Different Domains

1 - JustAnswer
1 - JustAnswer

... 1.Problem statement Develop a Program that will read the age from user input and store it in to a Integer Bag Data Structure. In addition that program should check the whether size of Bag exceeds the Limit. When User Inputs -1 program should leave the age input mode . then it should display followin ...
estimating a parameter . educated guess at the value point
estimating a parameter . educated guess at the value point

... • Suppose we wanted the estimate of p to be accurate to within ±0.03, with 99% confidence. • How big a sample should we take? • The procedure is very similar to what we do in the estimating a mean setting. We need ...
Constant-Time Local Computation Algorithms
Constant-Time Local Computation Algorithms

Robust Reinforcement Learning Control with Static and Dynamic
Robust Reinforcement Learning Control with Static and Dynamic

First Slide
First Slide

... • Circles represent random variables. • Lines represent statistical dependencies. • There is a corresponding equation that gives P(x1, x2, x3, y, z), but often it’s easier to understand things from the picture. • These tinker toys for probabilities let you build up, from simple, easy-to-understand p ...
Using Artificial Neural Network to Predict Collisions on Horizontal
Using Artificial Neural Network to Predict Collisions on Horizontal

A Two-Phase Approach Towards Identifying Argument Structure in
A Two-Phase Approach Towards Identifying Argument Structure in

... natural language. We have directly used the 300-dimensional vectors trained on part of the Google News dataset (Mikolov et al., 2013) 3 . We have used the sum of word vectors over words present in Text node to form a feature vector. To generate another feature vector, a similar process is repeated f ...
Wavelength management in WDM rings to maximize the
Wavelength management in WDM rings to maximize the

... with approximation ratio e−1 ≈ 1.58198 [24] (see also the discussion in [18]). The best known approximation algorithms for maxRPC (and maxPC) in undirected rings has approximation ratio 3/2 [17, 18] while an 11/7-approximation algorithm for maxRPC in bidirected rings is presented in [18]. Interestin ...
A Bucket Elimination Approach for Determining Strong
A Bucket Elimination Approach for Determining Strong

Efficient Computation of Range Aggregates against Uncertain
Efficient Computation of Range Aggregates against Uncertain

Probabilistic R5V2/3 Assessments
Probabilistic R5V2/3 Assessments

... times • This means random sampling (i.e. each distributed variable is randomly sampled and these values used in a trial calculation) ...
Planning with Different Forms of Domain
Planning with Different Forms of Domain

... A central issue in incorporating domain-dependent control knowledge into a planner is to identify the classes of knowledge to incorporate and to devise a means of representing and reasoning with this knowledge. In the past, planners such as TLPlan and TALplan have exploited domain-dependent temporal ...
An Investigation of Selection Hyper
An Investigation of Selection Hyper

... change characteristics. The simplest approach is to restart the search algorithm each time a change occurs. However, usually the change in the environment is not too drastic and information gained during the previous environments can be used to locate the new optima much quicker. The main problem in ...
convergence results for ant routing algorithms via stochastic approximation and optimization
convergence results for ant routing algorithms via stochastic approximation and optimization

Representation of Number in Animals and Humans: A Neural Model
Representation of Number in Animals and Humans: A Neural Model

Getting Started with PROC LOGISTIC
Getting Started with PROC LOGISTIC

... Schwartz Bayesian Criterion are also provided but are beyond the scope of this paper. Tests of the Local Null Hypotheses Tests of the ‘statistical significance’ of each independent variable are also provided. The Wald Chi-Square test (and its associated p-value) are printed along with the parameter ...
IJAI-13 - aut.upt.ro
IJAI-13 - aut.upt.ro

... Segmentation problems are based on the division of a given curve in a set of n segments (being each of these segments represented by a linear model, which points to another common naming convention for this process: piecewise linear representation, PLR) minimizing the representation error. This is a ...
A High-Performance Multi-Element Processing Framework on GPUs
A High-Performance Multi-Element Processing Framework on GPUs

... seamless transformation from a single processing algorithm to its multi-processing counterpart. However, only the runtime cost is hidden, the overhead is still exist. In other word, computational resources are still consumed to process this redundancy, and hence the overall efficiency is lowered. We ...
Idealizations of Uncertainty, and Lessons from Artificial Intelligence
Idealizations of Uncertainty, and Lessons from Artificial Intelligence

... The generality of the logical procedures is the essential aspect that leads to the description of these techniques as AI models, rather than simply domain-specific programs, in that a general (logically deductive) theory of reasoning is assumed. For extensibility of this methodology, it must be poss ...
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Pattern recognition

Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition systems are in many cases trained from labeled ""training"" data (supervised learning), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning).The terms pattern recognition, machine learning, data mining and knowledge discovery in databases (KDD) are hard to separate, as they largely overlap in their scope. Machine learning is the common term for supervised learning methods and originates from artificial intelligence, whereas KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use. Pattern recognition has its origins in engineering, and the term is popular in the context of computer vision: a leading computer vision conference is named Conference on Computer Vision and Pattern Recognition. In pattern recognition, there may be a higher interest to formalize, explain and visualize the pattern, while machine learning traditionally focuses on maximizing the recognition rates. Yet, all of these domains have evolved substantially from their roots in artificial intelligence, engineering and statistics, and they've become increasingly similar by integrating developments and ideas from each other.In machine learning, pattern recognition is the assignment of a label to a given input value. In statistics, discriminant analysis was introduced for this same purpose in 1936. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is ""spam"" or ""non-spam""). However, pattern recognition is a more general problem that encompasses other types of output as well. Other examples are regression, which assigns a real-valued output to each input; sequence labeling, which assigns a class to each member of a sequence of values (for example, part of speech tagging, which assigns a part of speech to each word in an input sentence); and parsing, which assigns a parse tree to an input sentence, describing the syntactic structure of the sentence.Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform ""most likely"" matching of the inputs, taking into account their statistical variation. This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns. A common example of a pattern-matching algorithm is regular expression matching, which looks for patterns of a given sort in textual data and is included in the search capabilities of many text editors and word processors. In contrast to pattern recognition, pattern matching is generally not considered a type of machine learning, although pattern-matching algorithms (especially with fairly general, carefully tailored patterns) can sometimes succeed in providing similar-quality output of the sort provided by pattern-recognition algorithms.
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