negative training-set instances
The influence of negative training set size on machine learning-based virtual screening
... to negative training instances should be taken into account during the prep- aration of machine learning experiments, as it might significantly influence the performance of particular ...the ...
9
The Impact of Topic Bias on Quality Flaw Prediction in Wikipedia
... reliable training data is the most important prerequisite for good predic- ...a set of examples that reliably represent a particular flaw, while on the other hand, we need counterexamples which reliably ...
10
Learning Protein–Protein Interaction Extraction using Distant Supervision
... Table 3 clearly indicates that positive to negative ratio on training data affects performance of a clas- sifier. Precision and recall strongly correlate with the pos/neg ratio seen in the training ...
8
Online Full Text
... rule set that tries to cover all of the training ...few training instances tend to be ...the training instances, their predictive accuracy on future test instances is ...
6
FEATURE ANALYSIS OF PUBLIC COMPLAINT HANDLING APPLICATION USING FODA
... We calculate the valence and the polarity of the English word or phrase w using a training corpus of approximately one hundred billion English words — the subset of the English Web that is indexed by the Google ...
30
A NOVEL EARLY WARNING SYSTEM USING FUZZY MULTIPLE ATTRIBUTE DECISION MAKING ALGORITHM AND METEOROLOGICAL DATA
... data set, which is 8,500,000 documents including the 4,250,000 positive and the 4,250,000 negative based on our English training data set which is 5,000,000 sentences comprising the 2,500,000 ...
42
Machine Learning Techniques for Anomaly Detection: An Overview
... The two typical unsupervised neural networks are self- organizing maps and adaptive resonance theory. They used similarity to group objects. They are adequate for intrusion detection tasks where normal behavior is ...
9
The complexity landscape of decompositional parameters for ILP
... ILP instances, i.e., ILP instances where all coefficients as well as all variable domains are assumed to be non-negative, ILP is known to be fixed-parameter tractable parameterized by the ...
20
Theoretical formulation and analysis of the deterministic dendritic cell algorithm
... As mentioned previously, applications of the DCA are referred to the area of anomaly detection. In AIS, one popular anomaly detection algo- rithm is known as the negative selection algorithm, which was shown to ...
28
Training a Neural Network in a Low Resource Setting on Automatically Annotated Noisy Data
... The noise-model outperforms the cleaning- model in the four lower-resource settings while the latter performs slightly better in the two scenarios with more data. With its access to the features in the noise cleaning ...
7
Multilingual Modal Sense Classification using a Convolutional Neural Network
... penhofer and Rehbein (2012) and follow-up work in Zhou et al. (2015) (henceforth, R&R and Z+). R&R induced modal sense classifiers from man- ual annotations on the MPQA corpus (Wiebe et al., 2005) using ...
10
Volume 17: Kommunikation in Verteilten Systemen 2009
... state, instances of the process appear for every client ...state, instances may either be abruptly ended (destroy instances) or, the second option is, that the process can’t be invoked, but existing ...
13
Classifying Unstructured Text Using Structured Training Instances and an Ensemble of Classifiers
... of training data being in a different format from the values that they will later ...feature set is rather generic, and as such it has been used as a starting point for value- based ...
16
Dependency Tree Kernels for Relation Extraction
... relation instances, we iterate over all pairs of entities oc- curring in the same ...labeled training set of potential relations, we define a tree kernel over dependency trees which we then use in an ...
7
Classification of Data Stream with Skewed Distribution
... Evaluation Metric: Appropriate choice of evaluation metrics is also important in this domain. Evaluation metrics play vital role in data mining; they are used to guide the algorithms to desired solution. Thus if ...
5
Automatically Building Training Examples for Entity Extraction
... get class extracted by the KEs. The presence of false negatives is then due to the low coverage of the KEs for the target class, e.g. the KEs were not able to ex- tract ‘Matthew Flynt’ and ‘Kerry Taylor’ as actors. ...
9
Deep word embeddings for visual speech recognition
... In this paper, we proposed a deep learning architecture for lipreading that is capable of attaining performance beyond state-of-the-art in the challenging LRW database. The ar- chitecture combines spatiotemporal ...
5
High-performance on-road vehicle detection with non-biased cascade classifier by weight-balanced training
... for training. It makes negative samples more difficult. The training cascade is set to focus on negative samples as the stage goes on, and the selected weak clas- sifiers become ...
7
IJCSMC, Vol. 3, Issue. 3, March 2014, pg.562 – 568 RESEARCH ARTICLE A Comparative Study on Performance Evalution of Eager versus Lazy Learning Methods
... into training sets and test sets in order to build a model and use it for the classification purpose ...The training set involves the various attributes having one as classifying ...test set ...
7
Learning Hierarchical Linguistic Descriptions of Visual Datasets
... assign instances to different nodes in the tree, whereas our approach learns an attribute hierarchy which is used to assign the instances to the nodes of the ...
9