Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.

Exploiting a probabilistic hierarchical model for generation

Abstract: Previous stochastic approaches to generation do not include a tree-based representation of syntax. While this may be adequate or even advantageous for some applications, other applications profit from using as much syntactic knowledge as is available, leaving to a stochastic model only those issues that are not determined by the grammar. We present initial results showing that a tree-based model derived from a tree-annotated corpus improves on a tree model derived from an unannotated corpus, and that a tree-based stochastic model with a hand-crafted grammar outperforms both.

Decision Forest: Combining the Predictions of Multiple Independent Decision Tree Models

Abstract: The techniques of combining the results of multiple classification models to produce a single prediction have been investigated for many years. In earlier applications, the multiple models to be combined were developed by altering the training set. The use of these so-called resampling techniques, however, poses the risk of reducing predictivity of the individual models to be combined and/or over fitting the noise in the data, which might result in poorer prediction of the composite model than the individual models. In this paper, we suggest a novel approach, named Decision Forest, that combines multiple Decision Tree models. Each Decision Tree model is developed using a unique set of descriptors. When models of similar predictive quality are combined using the Decision Forest method, quality compared to the individual models is consistently and significantly improved in both training and testing steps. An example will be presented for prediction of binding affinity of 232 chemicals to the estrogen receptor.

Complexity results for planning

Abstract: I describe several computational complexity results for planning, some of which identify tractable planning problems. The model of planning, called "propositional planning," is simple—conditions within operators are literals with no variables allowed. The different plan­ ning problems are defined by different restrictions on the preconditions and postconditions of operators. The main results are: Proposi­ tional planning is PSPACE-complete, even if operators are restricted to two positive (non-negated) preconditions and two postconditions, or if operators are restricted to one postcondi­ tion (with any number of preconditions). It is NP-complete if operators are restricted to positive postconditions, even if operators are restricted to one precondition and one posi­ tive postcondition. It is tractable in a few re­ stricted cases, one of which is if each opera­ tor is restricted to positive preconditions and one postcondition. The blocks-world problem, slightly modified, is a subproblem of this re­ stricted planning problem. 1 Introduction If the relationship between intelligence and computation is taken seriously, then intelligence cannot be explained by intractable theories because no intelligent creature has the time to perform intractable computations. Nor can intractable theories provide any guarantees about the performance of engineered systems. Presumably, robots don't have the time to perform intractable com­ putations either. Of course, heuristic theories are a valid approach if partial or approximate solutions are acceptable. How­ ever, my purpose is not to consider the relative merits of heuristic theories and tractable theories. Instead, I shall focus on formulating tractable planning problems. Planning is the reasoning task of finding a sequence of operators that achieve a goal from a given initial state. It is well-known that planning is intractable in general, and that several obstacles stand in the way [Chapman. 1987]. However, there are few results that provide clear dividing lines between tractable and in tractable planning. Below, I clarify a few of these dividing lines by analyzing the computational complexity of a planning problem and a variety of restricted versions, some of which are tractable. The model of planning, called "propositional planning ," is impoverished compared to working planners. It is intended to be a tool for theoretical analysis rather than programming convenience. Preconditions and post-conditions of operators are limited to being literals, i.e., letters or their negations. An initial state then can be represented as a finite set of letters, indicating that the corresponding conditions are initially true, and that all other relevant conditions are initially …

Resource-efficient machine learning in 2 KB RAM for the internet of things

Abstract: This paper develops a novel tree-based algorithm, called Bonsai, for efficient prediction on IoT devices - such as those based on the Arduino Uno board having an 8 bit ATmega328P microcontroller operating at 16 MHz with no native floating point support, 2 KB RAM and 32 KB read-only flash. Bonsai maintains prediction accuracy while minimizing model size and prediction costs by: (a) developing a tree model which learns a single, shallow, sparse tree with powerful nodes; (b) sparsely projecting all data into a low-dimensional space in which the tree is learnt; and (c) jointly learning all tree and projection parameters. Experimental results on multiple benchmark datasets demonstrate that Bonsai can make predictions in milliseconds even on slow microcontrollers, can fit in KB of memory, has lower battery consumption than all other algorithms while achieving prediction accuracies that can be as much as 30% higher than state-of-the-art methods for resource-efficient machine learning. Bonsai is also shown to generalize to other resource constrained settings beyond IoT by generating significantly better search results as compared to Bing's L3 ranker when the model size is restricted to 300 bytes. Bonsai's code can be downloaded from (BonsaiCode).

Multiple Tree Models for Occlusion and Spatial Constraints in Human Pose Estimation

Abstract: Tree-structured models have been widely used for human pose estimation, in either 2D or 3D. While such models allow efficient learning and inference, they fail to capture additional dependencies between body parts, other than kinematic constraints between connected parts. In this paper, we consider the use of multiple tree models, rather than a single tree model for human pose estimation. Our model can alleviate the limitations of a single tree-structured model by combining information provided across different tree models. The parameters of each individual tree model are trained via standard learning algorithms in a single tree-structured model. Different tree models can be combined in a discriminative fashion by a boosting procedure. We present experimental results showing the improvement of our approaches on two different datasets. On the first dataset, we use our multiple tree framework for occlusion reasoning. On the second dataset, we combine multiple deformable trees for capturing spatial constraints between non-connected body parts.