Add to ORKGWe discuss several approaches to similarity preserving coding of symbol sequences and possible connections of their distributed versions to metric embeddings. Interpreting sequence representation methods with embeddings can help develop an approach to their analysis and may lead to discovering useful properties
There are many types of sequences on which classification algorithms are applied. Sequences of symbo...
First we consider pair-wise distances for literal objects consisting of finite binary files. These f...
Abstract Background Sequence similarity networks are useful for classifying and characterizing biolo...
We analyze an approach to a similarity preserving coding of symbol sequences based on neural distrib...
Abstract: We discuss several approaches to similarity preserving coding of symbol sequences and poss...
String kernel-based machine learning methods have yielded great success in practical tasks of struct...
Efficient and expressive comparison of sequences is an essential procedure for learning with se-quen...
This paper introduces the sequence covering similarity, that we formally define for evaluating the s...
A growing number of measures of sequence similarity is being based on some underlying notion of rela...
Similarity search in sequence databases is ofparamount importance in bioinformatics research. As the...
irements. A simple and computationally very effective "distance" measure for sequences is ...
A system for determining semantic similarity of phrases (word sequences) uses weak supervision of ne...
The minimal-length encoding approach is applied to define concept of sequence similarity. A sequence...
Nowadays sequences of symbols are becoming more important, as they are the standard format for repre...
Computing the similarity between sequences is a very important challenge for many different data min...
There are many types of sequences on which classification algorithms are applied. Sequences of symbo...
First we consider pair-wise distances for literal objects consisting of finite binary files. These f...
Abstract Background Sequence similarity networks are useful for classifying and characterizing biolo...
We analyze an approach to a similarity preserving coding of symbol sequences based on neural distrib...
Abstract: We discuss several approaches to similarity preserving coding of symbol sequences and poss...
String kernel-based machine learning methods have yielded great success in practical tasks of struct...
Efficient and expressive comparison of sequences is an essential procedure for learning with se-quen...
This paper introduces the sequence covering similarity, that we formally define for evaluating the s...
A growing number of measures of sequence similarity is being based on some underlying notion of rela...
Similarity search in sequence databases is ofparamount importance in bioinformatics research. As the...
irements. A simple and computationally very effective "distance" measure for sequences is ...
A system for determining semantic similarity of phrases (word sequences) uses weak supervision of ne...
The minimal-length encoding approach is applied to define concept of sequence similarity. A sequence...
Nowadays sequences of symbols are becoming more important, as they are the standard format for repre...
Computing the similarity between sequences is a very important challenge for many different data min...
There are many types of sequences on which classification algorithms are applied. Sequences of symbo...
First we consider pair-wise distances for literal objects consisting of finite binary files. These f...
Abstract Background Sequence similarity networks are useful for classifying and characterizing biolo...