Binary classification problems

Lecture Artificial intelligence: Binary classifiers for multi-class classification problems. This lecture provides students with content including: classification; binary classification; multi-class classification; binary classifiers; support vector machines; perceptron; logistic regression;... Please refer to the detailed content of the lecture!

12p codabach1016 03-05-2024 4 0   Download

This paper presents research on detecting fake news based on news content and social context approach using machine learning. First of all, we analyze related concepts, methods of detecting fake news. Next, we model this task as a binary classification problem, representing news content and social context as feature vectors.

12p vigojek 02-02-2024 6 2   Download

In this paper, we propose a new Improvement Least Square - Support Vector Machine (called ILS-SVM) for binary classification problems with a class-vs-cluster strategy. Experimental results show that the ILS-SVM training time is faster than that of TSVM, and the ILS-SVM accuracy is better than LSTSVM and TSVM in most cases.

6p viplato 05-04-2022 17 1   Download

This paper proposes a new Weighted Structural - Support Vector Machine (called WS-SVM) for binary classification problems with a class-vs-clusters strategy. Experimental results show that WS-SVM could describe the tendency of the distribution of cluster information. Furthermore, both the theory and experiment show that the training time of the WS-SVM for classification problem has significantly improved compared to S-TWSVM.

14p nguaconbaynhay11 07-04-2021 14 2   Download

Many problems in bioinformatics involve classification based on features such as sequence, structure or morphology. Given multiple classifiers, two crucial questions arise: how does their performance compare, and how can they best be combined to produce a better classifier? A classifier can be evaluated in terms of sensitivity and specificity using benchmark, or gold standard, data, that is, data for which the true classification is known.

11p viwyoming2711 16-12-2020 11 0   Download

In terms of ideas, SVM uses tricks to map the original dataset to more dimensional spaces. Once mapped to a multidimensional space, SVM will review and select the most suitable superlattice to classify that data set.

9p kequaidan3 11-03-2020 10 1   Download

We propose a new formulation of the PP attachment problem as a 4-way classification which takes into account the argument or adjunct status of the PP. Based on linguistic diagnostics, we train a 4-way classifier that reaches an average accuracy of 73.9% (baseline 66.2%). Compared to a sequence of binary classifiers, the 4-way classifier reaches better performance and individuates a verb's arguments more accurately, thus improving the acquisition of a crucial piece of information for many NLP applications. ...

8p bunthai_1 06-05-2013 44 1   Download

In this work, we investigate the use of error-correcting output codes (ECOC) for boosting centroid text classifier. The implementation framework is to decompose one multi-class problem into multiple binary problems and then learn the individual binary classification problems by centroid classifier. However, this kind of decomposition incurs considerable bias for centroid classifier, which results in noticeable degradation of performance for centroid classifier. In order to address this issue, we use Model-Refinement to adjust this so-called bias. ...

4p hongvang_1 16-04-2013 55 3   Download

Detection and classification arise in signal processing problems whenever a decision is to be made among a finite number of hypotheses concerning an observed waveform. Signal detection algorithms decide whether the waveform consists of “noise alone” or “signal masked by noise.” Signal classification algorithms decide whether a detected signal belongs to one or another of prespecified classes of signals. The objective of signal detection and classification theory is to specify systematic strategies for designing algorithms which minimize the average number of decision errors.

15p longmontran 18-01-2010 71 9   Download