TL;DR: A method is presented that is asserted to provide all hypothetical ancestral character states that are consistent with describing the descent of the present-day character states in a minimum number of changes of state using a predetermined phylogenetic relationship among the taxa represented.
Abstract: Fitch, W. M. (Dept. of Physiological Chemistry, Univ. of Wisconsin, Madison, Wisconsin, 53706), 1971. Toward defining the course of evolution: minimum change for a specific tree topology. Syst. Zool., 20:406-416.-A method is presented that is asserted to provide all hypothetical ancestral character states that are consistent with describing the descent of the present-day character states in a minimum number of changes of state using a predetermined phylogenetic relationship among the taxa represented. The character states used as examples are the four messenger RNA nucleotides encoding the amino acid sequences of proteins, but the method is general. [Evolution; parsimonious trees.] It has been a goal of those attempting to deduce phylogenetic relationships from information on biological characteristics to find the ancestral relationship(s) that would permit one to account for the descent of those characteristics in a manner requiring a minimum number of evolutionary steps or changes. The result could be called the most parsimonious evolutionary tree and might be expected to have a high degree of correspondence to the true phylogeny (Camin and Sokal, 1965). It's justification lies in the most efficient use of the information available and does not presuppose that evolution follows a most parsimonious course. There are no known algorithms for finding the most parsimonious tree(s) apart from the brute force method of examining nearly every possible tree.' This is impractical for trees involving a dozen or more taxonomic units. Most numerical taxonomic procedures (Sokal and Sneath, 1963; Farris, 1969, 1970; Fitch and Margoliash, 1967) provide dendrograms that would be among the more parsimonious solutions; one just cannot be sure that a more parsimonious tree structure does not exist. Farris (1970) has explicitly considered the parsimony principle as a part of 'An elegant beginning to an attack on the problem has recently been published by Farris (1969) who developed a method which estimates the reliability of various characters and then weights the characters on the basis of that reliability. his method which, like the present method, has its roots in the Wagner tree (Wagner,
TL;DR: The subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed, and the relation between decision trees and neutral networks (NN) is also discussed.
Abstract: A survey is presented of current methods for decision tree classifier (DTC) designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, the subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed. The relation between decision trees and neutral networks (NN) is also discussed. >
TL;DR: It is shown that if the network is singly connected (e.g. tree-structured), then probabilities can be updated by local propagation in an isomorphic network of parallel and autonomous processors and that the impact of new information can be imparted to all propositions in time proportional to the longest path in the network.
TL;DR: This paper deals with a two-dimensional space-filling approach in which each node is a rectangle whose area is proportional to some attribute such as node size.
Abstract: The traditional approach to representing tree structures is as a rooted, directed graph with the root node at the top of the page and children nodes below the parent node with lines connecting them (Figure 1). Knuth (1968, p. 305-313) has a long discussion about this standard representation, especially why the root is at the top and he offers several alternatives including brief mention of a space-filling approach. However, the remainder of his presentation and most other discussions of trees focus on various node and edge representations. By contrast, this paper deals with a two-dimensional (2-d) space-filling approach in which each node is a rectangle whose area is proportional to some attribute such as node size.
TL;DR: A novel end-to-end neural model to extract entities and relations between them and compares favorably to the state-of-the-art CNN based model (in F1-score) on nominal relation classification (SemEval-2010 Task 8).
Abstract: We present a novel end-to-end neural model to extract entities and relations between them. Our recurrent neural network based model captures both word sequence and dependency tree substructure information by stacking bidirectional treestructured LSTM-RNNs on bidirectional sequential LSTM-RNNs. This allows our model to jointly represent both entities and relations with shared parameters in a single model. We further encourage detection of entities during training and use of entity information in relation extraction via entity pretraining and scheduled sampling. Our model improves over the stateof-the-art feature-based model on end-toend relation extraction, achieving 12.1% and 5.7% relative error reductions in F1score on ACE2005 and ACE2004, respectively. We also show that our LSTMRNN based model compares favorably to the state-of-the-art CNN based model (in F1-score) on nominal relation classification (SemEval-2010 Task 8). Finally, we present an extensive ablation analysis of several model components.