Meta-features are used to describe properties and characteristics of datasets and construct the feature space for meta-learning. Many of the different meta-features are defined for single variables and, therefore, are computed per feature of the dataset. Since datasets contain different numbers of fe...
For such, the first step is the creation of metabase, or metadataset, containing metafeatures extracted from several datasets along with the performance of a pool of candidate algorithm(s). The next step is the induction of machine learning metamodels using the metabase as input. These models...
Castellano, and A. Fanelli, Meta-Data: Characterization of Input Features for Meta-learning. Modeling Decisions for Artificial Intelligence, LNAI, 2005: p. 457-468.Castiello, C., Castellano, G., Fanelli, A.M.: Meta-data: Characterization of input features for meta-learning. In: Torra, V....
Summary: This paper proposes several novel methods, based on machine learning, to detect malware in executable files without any need for preprocessing, such as unpacking or disassembling. The basic method (Mal-ID) is a new static (form-based) analysis methodology that uses common segment analysis...
This gives us a baseline that we can use for a comparison with future methods. At first, several time series meta-features are calculated for each time series in a data set and are saved as a meta-feature data set. Although it is presented in the context of meta-learning, the guide...
New AI features for Ray-Ban Meta glasses help you remember things, translate speech in real time, answer questions about things you’re seeing and more.
In this paper, we evaluate the effectiveness of six types of meta features on two public data sets with SVM, a well established machine learning technique. The experimental results show that lexical and syntactic features are the most promising features for AA of online texts. Furthermore, a ...
Implementation of Meta AI's Segment Anything Model to do an automated image annotation of simple microscope images and a modified GAN to cluster the preprocessed images according to their qualitative features - stefanherdy/SAM-GAN-Clustering
Recent work on scene classification still makes use of generic CNN features in a rudimentary manner. In this paper, we present a novel pipeline built upon deep CNN features to harvest discriminative visual objects and parts for scene classification. We first use a region propo...
Results of the meta-analysis revealed training effectiveness sample-weighted mean ds of 0.60 (k = 15, N = 936) for reaction criteria, 0.63 (k = 234, N = 15,014) for learning criteria, 0.62 (k = 122, N = 15,627) for behavioral criteria, and 0.62 (k = 26, N = 1,748) for ...