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  2. Feature (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Feature_(machine_learning)

    Examples of categorical features include gender, color, and zip code. Categorical features typically need to be converted to numerical features before they can be used in machine learning algorithms. This can be done using a variety of techniques, such as one-hot encoding, label encoding, and ordinal encoding.

  3. Variational autoencoder - Wikipedia

    en.wikipedia.org/wiki/Variational_autoencoder

    The conditional VAE (CVAE), inserts label information in the latent space to force a deterministic constrained representation of the learned data. [ 15 ] Some structures directly deal with the quality of the generated samples [ 16 ] [ 17 ] or implement more than one latent space to further improve the representation learning.

  4. Autoencoder - Wikipedia

    en.wikipedia.org/wiki/Autoencoder

    An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.

  5. Optimal asymmetric encryption padding - Wikipedia

    en.wikipedia.org/wiki/Optimal_asymmetric...

    L is an optional label to be associated with the message (the label is the empty string by default and can be used to authenticate data without requiring encryption), PS is a byte string of k − m L e n − 2 ⋅ h L e n − 2 {\displaystyle k-\mathrm {mLen} -2\cdot \mathrm {hLen} -2} null-bytes.

  6. Inside–outside–beginning (tagging) - Wikipedia

    en.wikipedia.org/wiki/Inside–outside...

    IOB files have no place to put commonly-needed meta-data, such as the character encoding being used, the data source, internal location-markers, and so on. More powerful formats (most obviously XML , but even JSON or s-expressions ) can handle far more diverse annotations, have far less variation between implementations, and are often shorter ...

  7. Story generator - Wikipedia

    en.wikipedia.org/wiki/Story_generator

    A story generator or plot generator is a tool that generates basic narratives or plot ideas. The generator could be in the form of a computer program, a chart with multiple columns, a book composed of panels that flip independently of one another, or a set of several adjacent reels that spin independently of one another, allowing a user to select elements of a narrative plot.

  8. Convolutional code - Wikipedia

    en.wikipedia.org/wiki/Convolutional_code

    To convolutionally encode data, start with k memory registers, each holding one input bit.Unless otherwise specified, all memory registers start with a value of 0. The encoder has n modulo-2 adders (a modulo 2 adder can be implemented with a single Boolean XOR gate, where the logic is: 0+0 = 0, 0+1 = 1, 1+0 = 1, 1+1 = 0), and n generator polynomials — one for each adder (see figure below).

  9. Viterbi decoder - Wikipedia

    en.wikipedia.org/wiki/Viterbi_decoder

    A sample implementation of a path metric unit for a specific K=4 decoder. A path metric unit summarizes branch metrics to get metrics for paths, where K is the constraint length of the code, one of which can eventually be chosen as optimal.